A submerged plant reflectance inversion method, device, equipment and storage medium

Abstract

The present application relates to the technical field of submerged plant monitoring, and discloses a submerged plant reflectance inversion method, device, equipment and storage medium. In the method, multispectral satellite images of a reference water area are first acquired, and the spatial assignment of the inherent optical properties of the target water area is realized by using a water body optical characteristic processing model and a spatial assignment method, thereby providing stable and reliable optical input for the subsequent physical inversion model, and reducing the difficulty of acquiring in-situ depth measurement data or bottom material spectrum data of the target water area. Then, the water depth distribution data of the target water area is acquired by using laser altimetry satellite data and photon processing and spatial interpolation, thereby providing strong geometric constraints for the model. Finally, the two types of data and remote sensing reflectance images are input into the physical inversion model, the water body influence is accurately stripped by using the physical inversion model, and the final submerged plant reflectance data is obtained, thereby significantly improving the inversion accuracy and stability of the finally acquired submerged plant reflectance.

Description

Technical Field

[0001] This invention relates to the field of submerged plant monitoring technology, specifically to a method, apparatus, equipment, and storage medium for submerged plant reflectance inversion. Background Technology

[0002] Submerged plants in shallow aquatic ecosystems are important environmental indicator species and carbon sinks. Satellite remote sensing technology, with its wide-area and high-frequency monitoring capabilities, can serve as an effective means of monitoring the distribution, abundance, and health status of shallow aquatic ecosystems. Satellite remote sensing observations of submerged plants in shallow aquatic ecosystems primarily rely on the spectral characteristics of these plants.

[0003] The submerged plant monitoring method disclosed in the related technologies is based on acquiring multispectral satellite images, constructing a water depth-substrate inversion model through single-band, band ratio, or multi-band empirical models, and then training it by combining field depth measurement data and substrate spectral data to realize the inversion of water depth and substrate type, thereby identifying submerged plants.

[0004] Due to the complex optical characteristics of actual shallow water areas, the submerged plant monitoring methods disclosed in related technologies require a large amount of on-site water depth measurement data or bottom sediment spectral data, making it difficult to accurately invert the reflectance of submerged plants. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for retrieving the reflectance of submerged plants, thereby overcoming the shortcomings of existing submerged plant monitoring methods in the related art, which are difficult to accurately retrieve the reflectance of submerged plants.

[0006] In a first aspect, the present invention provides a method for retrieving the reflectance of submerged plants, the method comprising: Based on the acquired multispectral satellite imagery of the reference water area, the inherent optical property data of the target water area are obtained using a preset water body optical property processing model and spatial assignment method; the reference water area includes water areas whose coefficient of variation of water quality parameters with respect to the target water area is less than a preset threshold and which are connected to the target water area. Based on laser altimetry satellite data of the target water area, the water depth distribution data of the target water area is obtained by using photon point processing algorithms and spatial interpolation methods. Based on remote sensing reflectance image data of the target water area, combined with the inherent optical property data and water depth distribution data of the target water area, the submerged plant reflectance data of the target water area are obtained by optimizing the constructed physical inversion model.

[0007] Through the above implementation method, multispectral satellite images of the reference water area are first acquired. The inherent optical properties of the target water area are then spatially assigned using a water body optical feature processing model and spatial assignment method, providing stable and reliable optical input for the subsequent physical inversion model. This also reduces the difficulty of acquiring in-situ depth sounding data or bottom sediment spectral data of the target water area. Next, water depth distribution data of the target water area is obtained using laser altimeter satellite data, photon processing, and spatial interpolation, providing strong geometric constraints for the model. Finally, the two types of data and remote sensing reflectance images are input into the physical inversion model. The physical inversion model is used to accurately remove the influence of water bodies, obtaining the final submerged plant reflectance data, which significantly improves the inversion accuracy and stability of the final submerged plant reflectance.

[0008] In one optional implementation, the acquisition of multispectral satellite imagery of the reference water area, using a pre-defined water body optical property processing model and spatial assignment method, yields the inherent optical property data of the target water area, including: Based on multispectral satellite imagery of a reference water body, water body components are inverted using a pre-defined water body optical property processing model to obtain the inherent optical property data of the reference water body; the inherent optical property data of the reference water body includes the water absorption coefficient and backscattering coefficient of each component in the reference water body. Based on the inherent optical property data of the reference water body, the inherent optical property data of the target water body are obtained by converting the data using a spatial assignment method.

[0009] The above implementation method first acquires multispectral satellite images of a reference water area, then uses a water body optical feature processing model to perform water body component inversion, obtains the inherent optical attribute data of the reference water area, and then uses a spatial assignment method to synchronize the inherent optical attributes of the reference water area to the target water area, thereby obtaining the inherent optical attribute data of the target water area. This significantly reduces the difficulty of acquiring in-situ depth sounding data or bottom sediment spectral data of the target water area, provides accurate and unified fixed optical input parameters for the physical inversion model, and improves the stability and accuracy of subsequent submerged plant reflectance inversion.

[0010] In one optional implementation, the laser altimeter satellite data based on the target water area, using a photon point processing algorithm and spatial interpolation method, yields water depth distribution data for the target water area, including: Based on laser altimetry satellite data of the target water area, discrete depth sounding point data of the target water area are obtained by using photon denoising and photon extraction algorithms for the water surface and bottom. Based on the discrete sounding point data of the target water area, the true water depth point data of the target water area is obtained by using refraction correction and tidal correction algorithms, and is used as the water depth distribution data of the target water area.

[0011] Through the above implementation method, photon denoising and surface and bottom photon extraction are first performed on the laser altimeter satellite data of the target water area to obtain high-precision discrete sounding point data, thereby eliminating noise interference and ensuring the reliability of the obtained discrete sounding point data of the target water area. Then, refraction correction and tidal correction are performed to eliminate errors caused by water refraction and tidal fluctuations, which can quickly obtain real and accurate water depth distribution data, provide stable geometric input for subsequent physical models, and reduce the impact of water depth errors on subsequent submerged plant reflectance inversion.

[0012] In one optional implementation, the laser altimeter satellite data based on the target water area, using photon denoising and surface and bottom photon extraction algorithms, yields discrete depth sounding point data for the target water area, including: Based on satellite data of laser altimetry of the target water area, noise filtering and clustering algorithms are used to remove noise and obtain effective signal photon data of the water surface and bottom. Based on the effective signal photon data of the water surface, the water surface photon elevation data is obtained by using a statistical or Gaussian fitting algorithm for the photon height distribution. Based on the effective signal photon data from the underwater surface, underwater photon elevation data is obtained using a data search and threshold determination algorithm. By combining the surface photon elevation data and the bottom photon elevation data, discrete depth sounding point data of the target water area are obtained.

[0013] Through the above implementation method, noise filtering and clustering algorithms are first used to remove noise from the laser altimetry data, accurately retaining the effective signal photon data of the water surface and bottom; then, the water surface photon elevation data is determined by photon height distribution statistics or Gaussian fitting algorithms, and the bottom photon elevation data is obtained by combining data search and threshold determination, finally obtaining high-precision and high-reliability discrete sounding point data, providing a solid data foundation for subsequent water depth correction and areal water depth generation.

[0014] In one optional implementation, the remote sensing reflectance image data based on the target water area, combined with the inherent optical property data and water depth distribution data of the target water area, is optimized using a constructed physical inversion model to obtain submerged plant reflectance data of the target water area, including: The inherent optical properties and water depth distribution data of the target water area are set as fixed input parameters of the physical inversion model, and the typical reflectance spectrum of submerged plants is used as the initial value of the substrate to obtain the initialized physical inversion model. Based on the initialized physical inversion model, combined with the remote sensing reflectance image data of the target water area, the reflectance spectrum of the submerged plant substrate is iteratively adjusted using an optimization fitting algorithm to minimize the error between the simulated remote sensing reflectance and the reflectance of the remote sensing reflectance image data. Obtain the reflectance spectrum of the submerged plant substrate corresponding to the minimum error, and determine it as the true reflectance data of the submerged plants in the target water area.

[0015] Through the above implementation method, the inherent optical property data and water depth distribution data are first set as fixed input parameters of the physical inversion model. At the same time, the reflectance of submerged plants is used as the optimization object. The typical reflectance spectrum of submerged plants is used as the initial value and the true value is approximated through optimization fitting iteration. The modulation effect of water body and water depth is effectively removed. The final reflectance spectrum is the inherent property of submerged plants themselves, which greatly improves the stability, accuracy and physical interpretability of the inversion results, and facilitates the subsequent classification or evaluation of submerged plants.

[0016] In one alternative implementation, it further includes: Multiple water bodies connected to the target water body and whose adjacent distance is less than a preset value are selected as pre-selected water bodies; Based on multiple pre-selected water areas, and using a preset data filtering method, water areas with a coefficient of variation of water quality parameters less than a preset threshold are used as reference water areas.

[0017] Through the above implementation method, the pre-selected water areas are first screened based on connectivity and spatial distance, and then the reference water areas are determined based on the coefficient of variation of water quality parameters. This ensures that the optical properties of the reference water areas and the target water areas are highly uniform and there is no significant water quality gradient. This ensures that the inherent optical properties after transplantation are truly applicable and avoids deviations caused by differences in water quality. This lays the foundation for the stability and accuracy of the subsequent reflection inversion of submerged plants.

[0018] In one alternative implementation, it further includes: Based on the reflectance data of submerged plants in the target water area, the submerged plant species data and biomass estimation results are obtained by using the spectral feature matching method.

[0019] Through the above implementation methods, based on the submerged plant reflectance data of the target water area obtained by inversion, the influence of spectral modulation of the obtained submerged plant reflectance data on water composition and water depth is reduced. Then, the spectral feature matching method is used for identification and inversion, which can significantly improve the accuracy of submerged plant species identification and the precision of biomass estimation.

[0020] Secondly, the present invention provides a submerged plant reflectance inversion device, the device comprising: The first data acquisition module is used to obtain the inherent optical attribute data of the target water body based on the acquired multispectral satellite image of the reference water body, using a preset water body optical characteristic processing model and spatial assignment method; the reference water body includes water bodies whose coefficient of variation of water quality parameters with respect to the target water body is less than a preset threshold and which are connected to the target water body. The second data acquisition module is used to obtain the water depth distribution data of the target water area based on the laser altimetry satellite data of the target water area, using photon point processing algorithms and spatial interpolation methods. The result generation module is used to optimize the submerged plant reflectance data of the target water area based on the remote sensing reflectance image data of the target water area, combined with the inherent optical attribute data and water depth distribution data of the target water area, using the constructed physical inversion model.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the submerged plant reflectance inversion method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the submerged plant reflectance inversion method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first process of the submerged plant reflectance inversion method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the submerged plant reflectance inversion method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the third process of the submerged plant reflectance inversion method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the fourth process of the submerged plant reflectance inversion method according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a submerged plant reflectance inversion device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] The disclosed methods for monitoring submerged plants based on passive optical remote sensing mainly rely on their spectral characteristics. However, in complex water bodies, the acquired optical remote sensing signals are strongly influenced by the optical components of the water (chlorophyll, suspended matter, colored dissolved organic matter) and water depth, resulting in "same material, different spectra" or "different materials, same spectra," which severely limits the accuracy of submerged plant reflectance inversion. The main shortcomings are as follows: (1) When constructing a water depth-bottom sediment inversion model (such as a single-band, ratio, or multi-band empirical model) based on passive optical images in related technologies, a large amount of field depth measurement data or bottom sediment spectral data is required for training. The model has poor universality and is difficult to work stably in optically complex water bodies. (2) Passive optical image data alone cannot penetrate the water body to obtain accurate water depth, while laser altimetry data alone can only provide water depth at discrete points, cannot form a surface product, and cannot directly provide optical information of the water body.

[0029] Therefore, in order to overcome the shortcomings of the aforementioned related technologies, this application provides a method for retrieving the reflectance of submerged plants. First, multispectral satellite images of a reference water area are acquired. Then, a water body optical feature processing model and spatial assignment methods are used to spatially assign the inherent optical properties of the target water area, providing stable and reliable optical input for the subsequent physical inversion model. This also reduces the difficulty of acquiring in-situ depth sounding data or bottom sediment spectral data of the target water area. Next, laser altimeter satellite data, photon processing, and spatial interpolation are used to obtain the water depth distribution data of the target water area, providing strong geometric constraints for the model. Finally, these two types of data, along with remote sensing reflectance images, are input into the physical inversion model. The physical inversion model accurately removes the influence of water bodies to obtain the final submerged plant reflectance data, significantly improving the accuracy and stability of the final submerged plant reflectance retrieval.

[0030] According to an embodiment of the present invention, a method for retrieving the reflectance of submerged plants is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides a method for retrieving the reflectance of submerged plants, which can be used in a water environment monitoring server. Figure 1 This is a flowchart of the submerged plant reflectance inversion method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: S101. Based on the acquired multispectral satellite imagery of the reference water area, the inherent optical attribute data of the target water area are obtained using a preset water body optical characteristic processing model and spatial assignment method; wherein, the reference water area includes water areas whose coefficient of variation of water quality parameters with respect to the target water area is less than a preset threshold and which are connected to the target water area.

[0032] The reference water area is an optical deep-water region that is connected to the target water area and has similar water quality, used to retrieve the inherent optical property data of the target water area.

[0033] For example, obtaining reference water areas includes: Multiple water bodies connected to the target water body and whose adjacent distance is less than a preset value are selected as pre-selected water bodies; Based on multiple pre-selected water areas, and using a preset data filtering method, water areas with a coefficient of variation of water quality parameters less than a preset threshold are used as reference water areas.

[0034] First, pre-selected water areas are screened based on connectivity and spatial distance. Then, reference water areas are determined using the coefficient of variation of water quality parameters as a quantitative standard. This ensures that the optical properties of the reference water areas and the target water areas are highly uniform and there is no significant water quality gradient. This guarantees the true applicability of the inherent optical properties after transplantation and avoids deviations caused by differences in water quality. This lays the foundation for the stability and accuracy of subsequent submerged plant reflectance inversion.

[0035] Multispectral satellite imagery is a satellite-taken image that captures the reflectance characteristics of ground objects at different wavelengths through different spectral channels.

[0036] The water body optical property processing model is an example of an invertible water body absorption, scattering and other inherent optical properties. The water body optical property processing model can be implemented as a C2RCC (Case 2 Regional Coast Colour) model.

[0037] Specifically, the C2RCC model is a remote sensing processing model for complex water bodies, used to simultaneously perform atmospheric correction and water composition inversion on optical satellite imagery based on a radiative transfer model and neural networks. The C2RCC model generates a simulated dataset based on a radiative transfer model and performs rapid inversion by training a neural network. After receiving raw satellite data from the user, it outputs atmospherically corrected water reflectance, water absorption / scattering coefficients, chlorophyll concentration, suspended solids concentration, and other products (Brockmann et al., 2016).

[0038] In inland water bodies (such as lakes and reservoirs) and extremely shallow waters, the inversion accuracy of the C2RCC model may decrease significantly, even becoming uncorrelated with measured data. This is mainly because the parameter range of its training dataset differs from the actual conditions of these water bodies. When the water body contains high concentrations of colored dissolved organic matter, the inversion of products such as chlorophyll may be inaccurate. Studies show that the built-in neural network exhibits varying degrees of accuracy for different water body types.

[0039] Specifically, the image data of a reference water area acquired by optical satellite A is input into the C2RCC model, and the output parameters obtained include: (1) iop_apig: Absorption coefficient of phytoplankton particles at a wavelength of 443 nm (units) ); (2) iop_adet: Absorption coefficient of non-algal particulate matter at a wavelength of 443 nm (units) ); (3) iop_agelb: Absorption coefficient of colored soluble organic matter at a wavelength of 443 nm (units) ); (4) iop_bpart: Scattering coefficient of ocean particles at a wavelength of 443 nm (units) ); (5) iop_bwit: Scattering coefficient of white particle at 443 nm wavelength (unit: ...) ); (6) iop_adg: Absorption coefficient of "non-algal particles" + "colored dissolved organic matter" at a wavelength of 443 nm (units) This can be expressed as iop_adg = iop_adet + iop_agelb; (7) iop_atot: Total absorption coefficient of "planktonic algae particles" + "non-algae particles" + "colored dissolved organic matter" at a wavelength of 443 nm (units) This can be expressed as iop_atot = iop_apig + iop_adet + iop_agelb; (8) iop_btot: Total white particle scattering coefficient at 443 nm wavelength (unit: ...) This can be expressed as iop_btot = iop_bpat + iop_bwit; (9) conc_tsm: Concentration of total suspended solids (non-algae particles) (unit: g / ), expressed as conc_tsm=pow(iop_btot, 0.942)*1.06; (10) conc_chl: chlorophyll concentration (unit: g / ) ), expressed as conc_chl = pow(iop_apig,1.04)*21.

[0040] The scattering coefficients output from water body optical property processing models are difficult to use directly in physical inversion models. To ensure the accuracy of the initial values ​​input to the physical inversion model, it is necessary to convert the scattering coefficients into backscattering coefficients using the backscattering ratio function or volume scattering function. However, in most natural water bodies (excluding extremely turbid or high-bubble conditions), the common range for the backscattering ratio is 0.005. The value is 0.05, which has a large range of variation, making it difficult to directly convert between the two.

[0041] Therefore, this application directly uses the total suspended matter concentration generated by the water body optical property processing model to avoid the uncertainty caused by using the scattering coefficient. Furthermore, the chlorophyll concentration conc_chl obtained from the water body optical property processing model is used as the chlorophyll concentration (CHL) of the subsequent physical inversion model, the total suspended matter concentration conc_tsm is used as the suspended particulate matter concentration (SPM) of the subsequent physical inversion model, and the absorption coefficient of colored soluble organic matter at a wavelength of 443 nm obtained from the inversion is used as the absorption coefficient of colored soluble matter at 440 nm of the subsequent physical inversion model (aCDOM(440)).

[0042] The spatial assignment method is based on the assumption that "the optical properties of adjacent water bodies are spatially homogeneous when there is no significant pollution source input." The inherent optical attribute image obtained by inverting the reference water body is used as the inherent optical attribute data of the entire target water body, ensuring that the inherent optical attribute data between the target water body and the reference water body are consistent.

[0043] Inherent optical property data are the inherent optical characteristics of water bodies, which do not change with illumination or observation angle, including absorption coefficient and backscattering coefficient.

[0044] The coefficient of variation of water quality parameters is obtained based on the sample standard deviation and sample mean of the water quality parameters. It is an indicator of the spatial uniformity of water quality. At the same time, the smaller the value, the more stable the water quality and the more suitable it is as a reference area.

[0045] S102, based on laser altimetry satellite data of the target water area, uses photon point processing algorithms and spatial interpolation methods to obtain water depth distribution data of the target water area.

[0046] Laser altimetry satellite data is photon point cloud data of the height of the water surface and the seabed obtained by using a land elevation satellite to measure the height of the water surface and the seabed with lasers in the green band.

[0047] The photon point processing algorithm is used to denoise photon point cloud data, identify photons on the water surface, and identify photons at the bottom of the water.

[0048] Spatial interpolation methods obtain a water depth raster map of a target water area by interpolating discrete sounding point data.

[0049] Water depth distribution data is the water depth data at each location in the target water area, which can be implemented as a water depth raster map of the target water area.

[0050] S103. Based on the remote sensing reflectance image data of the target water area, combined with the inherent optical attribute data and water depth distribution data of the target water area, the submerged plant reflectance data of the target water area is obtained by optimizing the constructed physical inversion model.

[0051] Remote sensing reflectance image data is satellite remote sensing reflectance that has been atmospherically corrected and is used to reflect water information of a target water area.

[0052] The physical inversion model is a model that calculates the reflectivity of the substrate based on the principle of radiative transfer. It can be implemented as a BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images). By using the physical inversion model based on the bio-optical model and optimization techniques, the reflectivity of submerged plants obtained by simulation is continuously adjusted using optimization algorithms, thereby ensuring that the error between the simulated value output by the model and the satellite observation value is minimized. It can extract the optical characteristics of the water column, the depth of the substrate, and the composition ratio of no more than three types of substrate.

[0053] For example, the formula for calculating the remote sensing reflectance of the area exactly on the water surface in the physical inversion model is as follows: , in, Indicates wavelength The remote sensing reflectance of the water surface; Indicates wavelength Radiance reflectance just below the water surface; Indicates wavelength; and It is a parameter used to describe the propagation effect of radiance at the air-water interface, and can take values ​​of 0.52 and 1.7.

[0054] The radiance reflectance just below the water surface can be expressed as the sum of the water column reflectance and the substrate reflectance, as follows: , in, Indicates wavelength Water column reflectivity; Indicates wavelength The reflectivity of the underlying substrate; Indicates wavelength The reflectivity of deep-sea optical surfaces; The average underwater solar zenith angle of the entire remote sensing image can be calculated by combining the solar zenith angle on the water surface at the time of satellite imaging with Snell's law of refraction. The beam attenuation coefficient is expressed using the total absorption coefficient. and total backscattering coefficient Calculated; Indicates the depth of the substrate; Indicates wavelength Substrate albedo, used to represent the relative contribution of albedo to the albedo of the three different types of substrate. , and The sum; and This represents the proportionality coefficient, which takes values ​​of 1 and 1 / π respectively (Lee et al., 1999). The optical path length extension factor represents the scattered photons from the water column; The optical path length extension factor represents the scattered photons of the substrate.

[0055] wavelength Reflectivity of deep optical water It can be represented as: , , in, , and The values ​​are 0.084, 0.17, and 1, respectively. Indicates wavelength The percentage of the total absorption coefficient to the beam attenuation coefficient; Indicates wavelength The overall absorption coefficient is below; Indicates wavelength The total backscattering coefficient.

[0056] Beam attenuation coefficient This can be expressed as: , in, Indicates the overall absorption coefficient; This represents the total backscattering coefficient.

[0057] wavelength Substrate albedo It can be represented as: , , in, , and They represent wavelengths respectively. The albedo of three types of bottom sediments was obtained by inputting three text files, each representing a different type of bottom sediment, into the physical inversion model. The spectral resolution was 1 nm. The albedo data was obtained through actual measurement. , and These represent the relative contribution of the albedo to the corresponding substrate types.

[0058] Optical path extension factor of scattered photons from water column It can be represented as: , The optical path length extension factor of the scattered photons of the substrate can be expressed as: , Meanwhile, the physical inversion model uses optimization techniques to construct the objective function. This is used to measure the spectral distance between the simulated remote sensing reflectance and the actual remote sensing reflectance in the remote sensing image; the objective function is... The following conditions must be met: , in, Indicates wavelength Simulated remote sensing reflectance; Indicates wavelength The actual remote sensing reflectance of the remote sensing image below; Indicates wavelength The minimum value; Indicates wavelength The maximum value.

[0059] Since optimization techniques are a trial-and-error process, initialization with unknown initial values ​​is required. When the user inputs a set of initial values, including the initial values ​​of seven parameters such as chlorophyll concentration (CHL), suspended particulate matter concentration (SPM), the absorption coefficient of colored soluble substances at 440 nm (aCDOM(440)), the slope of the exponential curve describing aNAP(λ) (SNAP), substrate depth (H), and substrate composition ratios b0 and b1, the physical inversion model, within the upper and lower bounds of each parameter, uses IDL... ® (An encoding platform, Interactive Data Language) The platform's embedded CONSTRAINED_MIN function calculates the first-order partial derivative of each variable (approximately calculated using finite difference), repeating the calculation of the bio-optical model within a certain calculation step size to obtain the simulated remote sensing reflectance. This is then compared with the actual remote sensing reflectance of the remote sensing image until the objective function reaches a pre-set minimum value, at which point the calculation ends. The minimum value represents the optimal solution set after optimization, including: chlorophyll concentration, suspended particulate matter concentration, absorption coefficient of colored soluble substances at 440 nm, slope of the exponential curve describing the absorption coefficient of non-algal particulate matter, substrate depth, and substrate composition ratio.

[0060] Therefore, for optical deep-water and shallow-water scenarios, the physical inversion model also outputs a layer containing error metrics related to the inversion, as shown below: , Overall absorption coefficient It can be expressed as the pure water absorption coefficient. chlorophyll absorption coefficient Non-algal particulate matter absorption coefficient and the absorption coefficient of colored soluble organic matter The sum, expressed as follows: , Chlorophyll absorption coefficient The expression is as follows: , in, This represents the specific absorption coefficient of phytoplankton. Description The proportionality coefficient of the logarithmic relationship between CHL and Description and The logarithmic coefficients of the logarithmic relationship between the two are pre-set via a text file.

[0061] Non-algal particulate matter absorption coefficient The expression is as follows: , in, Description and The slope of the regression relationship between them. Description and The intercepts of the regression relationship between them were 0.031 and 0, respectively (Babin et al., 2003). express The slope of the exponential curve can be set to 0.079, which is obtained by optimization estimation based on the physical inversion model.

[0062] Colored soluble organic matter absorption coefficient The expression is as follows: , in, C and D Descriptions exponential coefficient The two scalar values ​​are 0.0067 and -0.3842, respectively. and When there is no explicit relationship between them, then C For the study area The average value, D It is 0.

[0063] Total backscattering coefficient It can be expressed as the backscattering coefficient of pure water. chlorophyll backscattering coefficient Backscattering coefficient of non-algal particles The sum: , chlorophyll backscattering coefficient This can be expressed as: , in, The specific backscattering coefficient of phytoplankton is preset via a text file.

[0064] Backscattering coefficient of non-algal particles This can be expressed as: , in, The specific backscattering coefficient of non-algal particles is preset via a text file. and Each represents a certain wavelength Below and The slope and intercept of the correlation between them.

[0065] The reflectance data of submerged plants is the true and inherent spectral reflectance of submerged plants after eliminating the influence of water body and water depth.

[0066] The methods provided in this application also include: Based on the reflectance data of submerged plants in the target water area, the submerged plant species data and biomass estimation results are obtained by using the spectral feature matching method.

[0067] The spectral feature matching method is a method that uses the intrinsic reflectance spectrum of submerged plants obtained by inversion to calculate, compare and classify the similarity between the spectrum of submerged plants and the spectrum of typical submerged plants in the standard spectral library, in order to identify vegetation types and invert biomass.

[0068] Submerged plant species data are submerged plant community types and species classification results identified by spectral feature matching methods, used to distinguish the spatial distribution of different submerged plant species in target waters.

[0069] Biomass estimation results are calculated based on the quantitative response relationship between the reflectance of submerged plants and biomass, and are used to assess the growth status and carbon sequestration capacity of submerged plants.

[0070] By using the submerged plant reflectance data of the target water area obtained through inversion, the influence of water composition and water depth on the spectral modulation of the obtained submerged plant reflectance data can be reduced. Then, the spectral feature matching method can be used for identification and inversion, which can significantly improve the accuracy of submerged plant species identification and the precision of biomass estimation.

[0071] This application provides a method for retrieving the reflectance of submerged plants. First, multispectral satellite images of a reference water area are acquired. Then, a water body optical feature processing model and spatial assignment method are used to spatially assign the inherent optical properties of the target water area, providing stable and reliable optical input for the subsequent physical inversion model. This also reduces the difficulty of acquiring in-situ depth sounding data or bottom sediment spectral data of the target water area. Next, laser altimeter satellite data, photon processing, and spatial interpolation are used to obtain the water depth distribution data of the target water area, providing strong geometric constraints for the model. Finally, the two types of data and remote sensing reflectance images are input into the physical inversion model. The physical inversion model is used to accurately remove the influence of water bodies to obtain the final submerged plant reflectance data, significantly improving the accuracy and stability of the final submerged plant reflectance retrieval.

[0072] This embodiment provides a method for retrieving the reflectance of submerged plants, which can be used in a water environment monitoring server. Figure 2 This is a flowchart of the submerged plant reflectance inversion method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: S201. Based on the acquired multispectral satellite imagery of the reference water area, the inherent optical attribute data of the target water area are obtained using a preset water body optical characteristic processing model and spatial assignment method; wherein, the reference water area includes water areas whose coefficient of variation of water quality parameters with respect to the target water area is less than a preset threshold and which are connected to the target water area.

[0073] Specifically, S201 above includes: S2011, based on multispectral satellite imagery of a reference water body, uses a pre-defined water body optical property processing model to invert water body components and obtain inherent optical property data of the reference water body; the inherent optical property data of the reference water body includes the water absorption coefficient and backscattering coefficient of each component in the reference water body. S2012, based on the inherent optical property data of the reference water body, the inherent optical property data of the target water body are obtained by transforming it using a spatial assignment method.

[0074] By first acquiring multispectral satellite imagery of a reference water area, and then using a water body optical feature processing model to perform water body component inversion, the inherent optical attribute data of the reference water area is obtained. Then, the inherent optical attributes of the reference water area are synchronized to the target water area using a spatial assignment method to obtain the inherent optical attribute data of the target water area. This significantly reduces the difficulty of acquiring in-situ depth sounding data or bottom sediment spectral data of the target water area, provides accurate and unified fixed optical input parameters for the physical inversion model, and improves the stability and accuracy of subsequent submerged plant reflectance inversion.

[0075] S202, based on laser altimeter satellite data of the target water area, uses photon point processing algorithms and spatial interpolation methods to obtain water depth distribution data of the target water area. For details, please refer to [link to relevant documentation]. Figure 1 S102 of the illustrated embodiment will not be described again here.

[0076] S203, based on remote sensing reflectance imagery data of the target water area, combined with the inherent optical properties and water depth distribution data of the target water area, is optimized using a constructed physical inversion model to obtain the submerged plant reflectance data of the target water area. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0077] This embodiment provides a method for retrieving the reflectance of submerged plants, which can be used in a water environment monitoring server. Figure 3 This is a flowchart of the submerged plant reflectance inversion method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: S301. Based on the acquired multispectral satellite imagery of a reference water area, and using a pre-defined water body optical property processing model and spatial assignment method, the inherent optical property data of the target water area are obtained. The reference water area includes water bodies whose water quality parameters have a coefficient of variation less than a pre-defined threshold and which are connected to the target water area. For details, please refer to [link to relevant documentation]. Figure 1 S101 of the illustrated embodiment will not be described again here.

[0078] S302 uses laser altimetry satellite data of the target water area and photon point processing algorithm and spatial interpolation method to obtain water depth distribution data of the target water area.

[0079] Specifically, S302 above includes: S3021 uses laser altimetry satellite data of the target water area and photon denoising and photon extraction algorithms for the water surface and bottom to obtain discrete depth sounding data of the target water area.

[0080] For example, S3021 above includes: a1, based on satellite data of laser altimetry of the target water area, uses noise filtering and clustering algorithms to remove noise, and obtains effective signal photon data of the water surface and bottom.

[0081] The laser altimeter satellite data was obtained by selecting areas with clear water and high seabed reflectivity (such as shallow coral reefs, beaches, shallow lakes, and clear rivers) as the study area.

[0082] Noise filtering and clustering algorithms can be implemented as density-based clustering (DBSCAN) and along-track sliding window statistical methods to filter noise in laser altimetry satellite data of the target water area and extract effective signal photons from the water surface and bottom.

[0083] a2, based on the effective signal photon data of the water surface, the water surface photon elevation data is obtained by using statistical or Gaussian fitting algorithms for the photon height distribution; a3, based on the effective signal photon data at the bottom of the water, uses data search and threshold determination algorithms to obtain the photon elevation data at the bottom of the water; a4, combining surface photon elevation data and bottom photon elevation data, yields discrete depth sounding data for the target water area.

[0084] First, noise filtering and clustering algorithms are used to remove noise from the laser altimetry data, accurately preserving the effective signal photon data of the water surface and bottom. Then, the water surface photon elevation data is determined by photon height distribution statistics or Gaussian fitting algorithms. At the same time, the bottom photon elevation data is obtained by combining data search and threshold determination. Finally, high-precision and high-reliability discrete sounding point data are obtained, providing a solid data foundation for subsequent water depth correction and surface water depth generation.

[0085] S3022, based on discrete sounding point data of the target water area, uses refraction correction and tidal correction algorithms to obtain the true water depth point data of the target water area, and uses it as the water depth distribution data of the target water area.

[0086] Refraction correction is used to correct the path bending effect caused by the refraction of light when it enters water from the air, thus obtaining the true water depth.

[0087] By subtracting the instantaneous tide level using tidal correction, the water surface height measured from discrete sounding points in the target water area is standardized relative to a reference ellipsoid to mean sea level or the lowest astronomical tide level. If the target water area is an inland water body (river, lake, etc.), tidal correction is not required.

[0088] First, photon denoising and surface and bottom photon extraction are performed on the laser altimeter satellite data of the target water area to obtain high-precision discrete sounding point data, thereby eliminating noise interference and ensuring the reliability of the obtained discrete sounding point data of the target water area. Then, refraction correction and tidal correction are performed to eliminate errors caused by water refraction and tidal fluctuations, which can quickly obtain real and accurate water depth distribution data, provide stable geometric input for subsequent physical models, and reduce the impact of water depth errors on subsequent submerged plant reflectance inversion.

[0089] S303, based on remote sensing reflectance imagery data of the target water area, combined with the inherent optical properties and water depth distribution data of the target water area, and optimized using a constructed physical inversion model, yields the submerged plant reflectance data of the target water area. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0090] This embodiment provides a method for retrieving the reflectance of submerged plants, which can be used in a water environment monitoring server. Figure 4 This is a flowchart of the submerged plant reflectance inversion method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: S401, based on the acquired multispectral satellite imagery of a reference water area, uses a pre-defined water body optical property processing model and spatial assignment method to obtain the inherent optical property data of the target water area; wherein, the reference water area includes water areas whose water quality parameters have a coefficient of variation of less than a pre-defined threshold and are connected to the target water area. For details, please refer to... Figure 1 S101 of the illustrated embodiment will not be described again here.

[0091] S402, based on laser altimeter satellite data of the target water area, uses photon point processing algorithms and spatial interpolation methods to obtain water depth distribution data of the target water area. For details, please refer to [link to relevant documentation]. Figure 1 S102 of the illustrated embodiment will not be described again here.

[0092] S403, based on remote sensing reflectance image data of the target water area, combined with the inherent optical attribute data and water depth distribution data of the target water area, and optimized using the constructed physical inversion model, the reflectance data of submerged plants in the target water area are obtained.

[0093] Specifically, S403 includes: S4031, set the inherent optical properties data and water depth distribution data of the target water area as fixed input parameters of the physical inversion model, and use the typical reflectance spectrum of submerged plants as the initial value of the bottom sediment to obtain the initialized physical inversion model; S4032, based on the initialized physical inversion model, combined with the remote sensing reflectance image data of the target water area, uses the optimization fitting algorithm to iteratively adjust the reflectance spectrum of the submerged plant substrate, so as to minimize the error between the simulated remote sensing reflectance and the reflectance of the remote sensing reflectance image data. S4033: Obtain the reflectance spectrum of the submerged plant substrate corresponding to the minimum error, and determine it as the true reflectance data of the submerged plants in the target water area.

[0094] By first setting the inherent optical property data and water depth distribution data as fixed input parameters for the physical inversion model, and taking the reflectance of submerged plants as the optimization object, and using the typical reflectance spectrum of submerged plants as the initial value, the model approximates the true value through optimization fitting iteration. This effectively removes the modulation effect of water body and water depth, and the final reflectance spectrum is the inherent property of submerged plants themselves. This greatly improves the stability, accuracy and physical interpretability of the inversion results, making it convenient for subsequent classification or evaluation of submerged plants.

[0095] This embodiment also provides a submerged plant reflectance inversion device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0096] This embodiment provides a device for retrieving the reflectance of submerged plants, such as... Figure 5 As shown, it includes: The first data acquisition module 510 is used to obtain the inherent optical attribute data of the target water body based on the acquired multispectral satellite image of the reference water body, using a preset water body optical characteristic processing model and spatial assignment method; the reference water body includes water bodies whose coefficient of variation of water quality parameters with respect to the target water body is less than a preset threshold and which are connected to the target water body. The second data acquisition module 520 is used to obtain water depth distribution data of the target water area based on laser altimetry satellite data of the target water area using photon point processing algorithm and spatial interpolation method; The result generation module 530 is used to optimize the submerged plant reflectance data of the target water area based on the remote sensing reflectance image data of the target water area, combined with the inherent optical attribute data and water depth distribution data of the target water area, and to obtain the reflectance data of the target water area by using the constructed physical inversion model.

[0097] In some optional implementations, the first data acquisition module 510 includes: The data inversion unit is used to invert water components based on multispectral satellite imagery of a reference water body and a pre-defined water optical property processing model to obtain the inherent optical property data of the reference water body. The inherent optical property data of the reference water body includes the water absorption coefficient and backscattering coefficient of each component in the reference water body. The spatial assignment unit is used to transform the intrinsic optical property data of the target water body based on the intrinsic optical property data of the reference water body using the spatial assignment method.

[0098] In some optional implementations, the second data acquisition module 520 includes: The data extraction unit is used to obtain discrete depth sounding point data of the target water area based on laser altimetry satellite data of the target water area by using photon denoising and photon extraction algorithms for the water surface and bottom. The data processing unit is used to obtain the true water depth data of the target water area based on the discrete sounding point data of the target water area using refraction correction and tidal correction algorithms, and to serve as the water depth distribution data of the target water area.

[0099] In some optional implementations, the data extraction unit includes: The denoising subunit is used to denoise laser altimetry satellite data based on the target water area by using noise filtering and clustering algorithms to obtain effective signal photon data of the water surface and bottom. The first extraction subunit is used to obtain water surface photon elevation data based on effective signal photon data from the water surface using statistical or Gaussian fitting algorithms for photon height distribution. The second extraction subunit is used to obtain underwater photon elevation data based on the effective signal photon data at the bottom of the water using data search and threshold determination algorithms. The data output unit is used to integrate surface photon elevation data and bottom photon elevation data to obtain discrete depth sounding point data of the target water area.

[0100] In some alternative implementations, the result generation module 530 includes: The model initialization unit is used to set the inherent optical property data and water depth distribution data of the target water area as fixed input parameters of the physical inversion model, and to use the typical reflectance spectrum of submerged plants as the initial value of the bottom sediment to obtain the initialized physical inversion model. The iterative optimization unit is used to iteratively adjust the reflectance spectrum of the submerged plant substrate based on the initialized physical inversion model and combined with the remote sensing reflectance image data of the target water area, using the optimal fitting algorithm to minimize the error between the simulated remote sensing reflectance and the reflectance of the remote sensing reflectance image data. The result output unit is used to obtain the reflectance spectrum of the submerged plant substrate corresponding to the minimum error, and to determine it as the true reflectance data of the submerged plants in the target water area.

[0101] In some alternative implementations, it also includes: The first filtering unit is used to obtain multiple water bodies that are connected to the target water body and whose adjacent distance is less than a preset value, and each of them is used as a pre-selected water body. The second screening unit is used to select water bodies with a coefficient of variation of water quality parameters that is less than a preset threshold as reference water bodies based on multiple pre-selected water bodies and using a preset data screening method.

[0102] In some alternative implementations, it also includes: The results analysis module is used to obtain submerged plant species data and biomass estimation results for the target water area based on the submerged plant reflectance data and spectral feature matching method.

[0103] The submerged plant reflectance inversion device provided in this embodiment of the invention can execute the submerged plant reflectance inversion method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0104] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0105] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0106] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0107] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the submerged plant reflectance inversion method of the embodiments of the present invention.

[0108] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0109] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the submerged plant reflectance inversion method shown in the above embodiments is implemented.

[0110] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0111] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for inverting the reflectance of submerged plants, characterized in that, The method includes: Based on the acquired multispectral satellite imagery of the reference water area, the inherent optical property data of the target water area are obtained using a preset water body optical property processing model and spatial assignment method; the reference water area includes water areas whose coefficient of variation of water quality parameters with respect to the target water area is less than a preset threshold and which are connected to the target water area. Based on laser altimetry satellite data of the target water area, the water depth distribution data of the target water area is obtained by using photon point processing algorithms and spatial interpolation methods. Based on remote sensing reflectance image data of the target water area, combined with the inherent optical property data and water depth distribution data of the target water area, the submerged plant reflectance data of the target water area are obtained by optimizing the constructed physical inversion model.

2. The method according to claim 1, characterized in that, The multispectral satellite imagery of the acquired reference water area is used to obtain the inherent optical attribute data of the target water area through a pre-defined water body optical property processing model and spatial assignment method, including: Based on multispectral satellite imagery of a reference water body, water body components are inverted using a pre-defined water body optical property processing model to obtain the inherent optical property data of the reference water body; the inherent optical property data of the reference water body includes the water absorption coefficient and backscattering coefficient of each component in the reference water body. Based on the inherent optical property data of the reference water body, the inherent optical property data of the target water body are obtained by converting the data using a spatial assignment method.

3. The method according to claim 1, characterized in that, The laser altimetry satellite data based on the target water area, using photon point processing algorithms and spatial interpolation methods, yields water depth distribution data for the target water area, including: Based on laser altimetry satellite data of the target water area, discrete depth sounding point data of the target water area are obtained by using photon denoising and photon extraction algorithms for the water surface and bottom. Based on the discrete sounding point data of the target water area, the true water depth point data of the target water area is obtained by using refraction correction and tidal correction algorithms, and is used as the water depth distribution data of the target water area.

4. The method according to claim 3, characterized in that, The laser altimetry satellite data based on the target water area is used to obtain discrete depth sounding point data of the target water area through photon denoising and surface and bottom photon extraction algorithms, including: Based on satellite data of laser altimetry of the target water area, noise filtering and clustering algorithms are used to remove noise and obtain effective signal photon data of the water surface and bottom. Based on the effective signal photon data of the water surface, the water surface photon elevation data is obtained by using a statistical or Gaussian fitting algorithm for the photon height distribution. Based on the effective signal photon data from the underwater surface, underwater photon elevation data is obtained using a data search and threshold determination algorithm. By combining the surface photon elevation data and the bottom photon elevation data, discrete depth sounding point data of the target water area are obtained.

5. The method according to claim 1, characterized in that, The remote sensing reflectance image data based on the target water area, combined with the inherent optical property data and water depth distribution data of the target water area, is optimized using a constructed physical inversion model to obtain the submerged plant reflectance data of the target water area, including: The inherent optical properties and water depth distribution data of the target water area are set as fixed input parameters of the physical inversion model, and the typical reflectance spectrum of submerged plants is used as the initial value of the substrate to obtain the initialized physical inversion model. Based on the initialized physical inversion model, combined with the remote sensing reflectance image data of the target water area, the reflectance spectrum of the submerged plant substrate is iteratively adjusted using an optimization fitting algorithm to minimize the error between the simulated remote sensing reflectance and the reflectance of the remote sensing reflectance image data. Obtain the reflectance spectrum of the submerged plant substrate corresponding to the minimum error, and determine it as the true reflectance data of the submerged plants in the target water area.

6. The method according to claim 1, characterized in that, Also includes: Multiple water bodies connected to the target water body and whose adjacent distance is less than a preset value are selected as pre-selected water bodies; Based on multiple pre-selected water areas, and using a preset data filtering method, water areas with a coefficient of variation of water quality parameters less than a preset threshold are used as reference water areas.

7. The method according to any one of claims 1 to 6, characterized in that, Also includes: Based on the reflectance data of submerged plants in the target water area, the submerged plant species data and biomass estimation results are obtained by using the spectral feature matching method.

8. A device for retrieving the reflectance of submerged plants, characterized in that, The device includes: The first data acquisition module is used to obtain the inherent optical attribute data of the target water body based on the acquired multispectral satellite image of the reference water body, using a preset water body optical characteristic processing model and spatial assignment method; the reference water body includes water bodies whose coefficient of variation of water quality parameters with respect to the target water body is less than a preset threshold and which are connected to the target water body. The second data acquisition module is used to obtain the water depth distribution data of the target water area based on the laser altimetry satellite data of the target water area, using photon point processing algorithms and spatial interpolation methods. The result generation module is used to optimize the submerged plant reflectance data of the target water area based on the remote sensing reflectance image data of the target water area, combined with the inherent optical attribute data and water depth distribution data of the target water area, using the constructed physical inversion model.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the submerged plant reflectance inversion method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the submerged plant reflectance inversion method according to any one of claims 1 to 7.

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