Chlorophyll concentration profile three-dimensional detection method and system

Abstract

The invention provides a chlorophyll a concentration profile three-dimensional detection method and system, and relates to the technical field of remote sensing data processing. Acquiring satellite image data and water body photon data of a regional water body, and dividing photons of the regional water body into water surface photons and underwater photons; sea wave fitting reconstruction is carried out, and a water body photon refraction correction model is constructed; correcting the underwater photon coordinates to obtain refraction correction coordinates; performing nonlinear correction on photon radiation transmission through an underwater profile detection model to obtain a volume scattering coefficient; establishing an underwater concentration model in combination with the refraction correction coordinate and the volume scattering coefficient; modeling the water surface chlorophyll a concentration by using a satellite image wave band, and calibrating an underwater concentration model to obtain a final underwater concentration model; and finally, combining the water surface and underwater chlorophyll a concentration models to obtain the three-dimensional chlorophyll a concentration of the regional water body. According to the invention, the detection precision and reliability are improved, and the vertical and horizontal distribution of chlorophyll a in a water body can be reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing data processing, and more specifically, to a three-dimensional detection method and system for chlorophyll-a concentration profile. Background Art

[0002] At present, as an important parameter reflecting the ecological health status of water bodies, chlorophyll-a concentration is widely used in fields such as marine monitoring, environmental protection, and resource management. At the same time, the change of chlorophyll-a concentration often directly affects the balance of the local ecosystem. Therefore, the accurate detection of chlorophyll-a concentration in regional water bodies is of great significance for environmental monitoring and scientific research.

[0003] In the prior art, the detection means of chlorophyll-a concentration generally adopt satellite remote sensing or on-site sampling. The above two methods mainly provide two-dimensional surface data of chlorophyll-a concentration, and it is difficult to effectively obtain the vertical distribution information inside the water body. Moreover, due to the dynamic complexity of the water body, both the detection accuracy and the spatial coverage are greatly limited. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the detection accuracy and spatial coverage of chlorophyll-a concentration.

[0005] To solve the above problems, the present invention provides a three-dimensional detection method and system for chlorophyll-a concentration profile.

[0006] In a first aspect, the present invention provides a three-dimensional detection method for chlorophyll-a concentration profile, including: Obtain satellite image data and water body photon data of a regional water body, where the water body photon data includes the original coordinates of photons in a preset point cloud coordinate system, and divide the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model; Based on the original coordinates of the surface photons, perform wave fitting reconstruction of sea waves, construct a water body photon refraction correction model, and correct the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons; Through an underwater profile detection model, perform non-linear correction on the photon radiation transmission of the regional water body to obtain the volume scattering coefficient of the regional water body at different depths; Based on the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the regional water body, establish an initial underwater concentration model of the regional water body; Model the surface chlorophyll-a concentration of the regional water body according to the image band of the satellite image data to obtain the surface chlorophyll-a concentration model of the regional water body; Calibrate the initial underwater concentration model according to the image bands of the satellite image data, and use the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; Obtain the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model.

[0007] Optionally, the step of dividing the photons of the regional water body into surface photons and underwater photons according to the piecewise statistical model includes: Map the original coordinates of the photons of the regional water body along the satellite orbit distance to a two-dimensional space to obtain the elevation-orbit distance distribution map of the photons; Slice the elevation-orbit distance distribution map along the elevation direction to obtain the elevation density histogram of the photons; Obtain the surface center elevation of the regional water body according to the elevation density histogram; Divide the photons into the surface photons and the underwater photons according to the surface center elevation.

[0008] Optionally, the step of constructing a water body photon refraction correction model by performing a sea wave fitting reconstruction based on the original coordinates of the surface photons includes: Determine the amplitude, angular frequency, direction angle, and wave phase of the sea wave according to the surface wind speed of the regional water body; Construct a sea wave model of the regional water body according to the amplitude, angular frequency, direction angle, and wave phase of the sea wave; Through the sea wave model, combine the original coordinates of the surface photons to perform ray tracing on the original spatial transmission path of each photon in the regional water body, and construct a water body photon refraction correction model.

[0009] Optionally, the step of correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons includes: Determine the spatial geometric relationships between the surface slope angle, incident angle, and refraction angle of the regional water body and the original spatial transmission path according to the water body photon refraction model; Determine the elevation displacement and orbit distance displacement of each underwater photon according to the spatial geometric relationships; Correct the original coordinates of the underwater photons according to the elevation displacement and the orbit distance displacement to obtain the refraction correction coordinates.

[0010] Optionally, the step of performing a non-linear correction on the photon radiation transmission of the regional water body through an underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths includes: Through the underwater profile detection model, iteratively correct the underwater photons to obtain the actual photon number sequence of the water body in the region at different depths; According to the original coordinates of the underwater photons, determine the depth of the underwater photons, and based on the depth, combined with preset radar parameters and water body parameters, determine the expected photon number sequence of the water body in the region at different depths; According to the actual photon number sequence and the expected photon number sequence of the water body in the region at different depths, determine the volume scattering coefficient of the water body in the region at different depths.

[0011] Optionally, establishing the initial underwater concentration model of the water body in the region according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the water body in the region includes: According to the refraction correction coordinates of the underwater photons, determine the correction depth of the underwater photons; Based on the correction depth, determine the mathematical model among the correction depth, the reflection angle corresponding to the correction depth, and the volume scattering coefficient; According to the mathematical model and combined with the wavelength correlation coefficient of the laser wavelength, establish the initial underwater concentration model.

[0012] Optionally, modeling the chlorophyll a concentration on the water surface of the water body in the region according to the image band of the satellite image data to obtain the chlorophyll a concentration model on the water surface of the water body in the region includes: Preprocess the satellite image data, and perform band extraction on the preprocessed satellite image data to obtain band images corresponding to the blue band and the green band; According to the band images corresponding to the blue band and the green band, combined with the remote sensing reflectance value of the satellite image data, determine the chlorophyll a concentration model on the water surface.

[0013] Optionally, calibrating the initial underwater concentration model according to the image band of the satellite image data, and using the calibrated initial underwater concentration model as the final underwater concentration model of the water body in the region includes: According to the chlorophyll a concentration model on the water surface, determine the chlorophyll a concentration on the water surface; According to the image band of the satellite image data, determine the particulate scattering coefficient on the water surface; Input the chlorophyll a concentration on the water surface and the particulate scattering coefficient on the water surface into the initial underwater concentration model to correct the wavelength correlation coefficient to obtain a corrected wavelength correlation coefficient; Calibrate the initial underwater concentration model according to the corrected wavelength correlation coefficient, and use the calibrated initial underwater concentration model as the final underwater concentration model of the water body in the region.

[0014] Optionally, obtaining the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model includes: Determining the surface chlorophyll a concentration of the regional water body on the water surface according to the surface chlorophyll a concentration model; Determining the underwater chlorophyll a concentration of the regional water body in the vertical profile according to the final underwater concentration model; Taking the surface chlorophyll a concentration and the underwater chlorophyll a concentration as the three-dimensional chlorophyll a concentration of the regional water body.

[0015] In a second aspect, the present invention provides a three-dimensional detection system for chlorophyll a concentration profile, including: A data acquisition unit, configured to acquire satellite image data and water body photon data of a regional water body, wherein the water body photon data includes the original coordinates of photons in a preset point cloud coordinate system, and divides the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model; A refraction correction unit, configured to perform wave fitting reconstruction on the basis of the original coordinates of the surface photons, construct a water body photon refraction correction model, and correct the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons; A non-linear correction unit, configured to perform non-linear correction on the photon radiation transmission of the regional water body through an underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths; A first model establishment unit, configured to establish an initial underwater concentration model of the regional water body according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the regional water body; A second model establishment unit, configured to model the surface chlorophyll a concentration of the regional water body according to the image band of the satellite image data to obtain a surface chlorophyll a concentration model of the regional water body; A model correction unit, configured to calibrate the initial underwater concentration model according to the image band of the satellite image data, and use the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; A concentration output unit, configured to obtain the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model.

[0016] The three-dimensional detection method and system for chlorophyll a concentration profile of the present invention divide photon data into surface photons and underwater photons through a segmented statistical model, and meticulously divide the photons to accurately identify and extract information from different parts of the water body. Since surface photons may be affected by factors such as direct sunlight and waves, while underwater photons provide the actual information inside the water body, dividing the photons helps reduce the interference of surface phenomena on underwater data and improve the detection accuracy. Then, using the original coordinates of the surface photons for wave fitting reconstruction of ocean waves, a photon refraction correction model for the water body is constructed to correct the original coordinates of the underwater photons, correcting the position error caused by water body refraction and improving the accuracy of the underwater photon coordinates, providing a guarantee for the detection accuracy in the depth direction. Then, through the underwater profile detection model, non-linear correction is performed on photon radiation transmission to recover the lost photon signals, making the detection data closer to the actual situation. On this basis, combined with satellite image data, a surface chlorophyll a concentration model is established, enabling the detection method to be extended from a two-dimensional surface to a three-dimensional space, and providing a large range of surface data through satellite images. Finally, by calibrating the initial underwater concentration model and using the calibrated model as the final underwater concentration model, the detection accuracy in the depth direction is further optimized. By combining the concentration models of the surface and underwater, the three-dimensional chlorophyll a concentration distribution of the regional water body can be accurately provided. The present invention not only improves the detection accuracy and reliability, but also can more comprehensively reflect the vertical and horizontal distributions of chlorophyll a in the water body, improving the spatial coverage of the three-dimensional detection of chlorophyll a concentration profile and providing a more accurate and comprehensive data basis for environmental monitoring and scientific research. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the three-dimensional detection method for chlorophyll a concentration profile according to an embodiment of the present invention; Figure 2 is a schematic diagram of the original data collected by a photon counting lidar according to an embodiment of the present invention; Figure 3 is a schematic diagram of an elevation density histogram according to an embodiment of the present invention; Figure 4 is a schematic diagram of the spatial structure of the displacement error caused by water body refraction according to an embodiment of the present invention; Figure 5 is a schematic diagram of counting the number of photons in a statistical underwater sliding window according to an embodiment of the present invention; Figure 6 is a schematic diagram of the three-dimensional detection of chlorophyll a concentration by active and passive data fusion according to an embodiment of the present invention; Figure 7 is a structural block diagram of the three-dimensional detection system for chlorophyll a concentration profile according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of specific embodiments of the present invention with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0019] It should be understood that the various steps recorded in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0020] As used herein, the term "including" and its variants are open-ended, that is, "including but not limited to"; the term "based on" is "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order or mutual dependency relationship of the functions performed by these devices, modules, or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless clearly stated otherwise in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] As Figure 1 shown, a three-dimensional detection method for chlorophyll a concentration profile provided by an embodiment of the present invention includes: Obtaining satellite image data and water body photon data of a regional water body, where the water body photon data includes the original coordinates of photons in a preset point cloud coordinate system, and dividing the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model.

[0024] Specifically, first, satellite image data and photon data of the water body are collected. The satellite image data provides macroscopic information on the water surface, while the photon data contains the original coordinates of photons in the preset point cloud coordinate system, and this data provides information at the microscopic level inside the water body. According to the piecewise statistical model, the photon data is further divided into surface photons and underwater photons, which helps to more accurately identify and extract the photon signals on the water surface and underwater.

[0025] Based on the original coordinates of the surface photons, a sea wave fitting reconstruction is performed to construct a water body photon refraction correction model, and according to the water body photon refraction model, the original coordinates of the underwater photons are corrected to obtain the refraction correction coordinates of the underwater photons.

[0026] Specifically, the original coordinates of the surface photons are used to perform the fitting reconstruction of the sea wave. In a preferred embodiment of the present invention, a sea wave spectrum model such as the JONSWAP model can be used to simulate the characteristics of sea waves. Based on the surface photon data set, a high-precision piecewise sea wave fitting reconstruction is performed using the JONSWAP model, and through this model, a water body photon refraction correction model can be constructed.

[0027] Through an underwater profile detection model, a non-linear correction is performed on the photon radiation transmission of the regional water body to obtain the volume scattering coefficient of the regional water body at different depths.

[0028] Specifically, through the surface signal photons, a water body photon refraction model based on the sea wave spectrum model is constructed to realize the coordinate correction of the water body photons, so as to obtain the refraction-corrected coordinates, thereby correcting the position deviation caused by refraction when photons propagate in the water body.

[0029] Based on the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the regional water body, an initial underwater concentration model of the regional water body is established.

[0030] Specifically, an underwater profile detection model is used to perform a non-linear correction on the photon radiation transmission. In a preferred embodiment of the present invention, technologies such as the Richardson-Lucy Deconvolution algorithm can be adopted to correct the non-linearity of radiation transmission caused by the after-pulse effect, so as to restore the non-linearity of radiation transmission caused by the after-pulse effect.

[0031] Based on the image band of the satellite image data, a model of the surface chlorophyll a concentration of the regional water body is established to obtain the surface chlorophyll a concentration model of the regional water body.

[0032] Specifically, a model for the surface chlorophyll a concentration of the regional water body is established based on the image bands of satellite image data. Among them, multispectral satellite images are used to assist in calibrating the surface chlorophyll a concentration parameters, so as to combine satellite image data with photon data to improve the accuracy of detection.

[0033] Calibrate the initial underwater concentration model according to the image bands of the satellite image data, and use the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body.

[0034] Specifically, use the image bands of satellite image data to calibrate the initial underwater concentration model, and use the calibrated model as the final underwater concentration model to calculate the vertical distribution of chlorophyll a concentration, ensuring the accuracy and reliability of the underwater concentration model and improving the estimation accuracy of underwater chlorophyll a concentration.

[0035] Obtain the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model.

[0036] Specifically, obtain the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model. This step involves matching photon sounding data and multispectral images to the same coordinate system and constructing a classification model to predict the chlorophyll a concentration values corresponding to pixels at different water depths, ultimately achieving high-precision and high-efficiency three-dimensional detection of the chlorophyll a concentration in the water body.

[0037] The three-dimensional detection method for chlorophyll a concentration profile of the present invention divides photon data into surface photons and underwater photons through a segmented statistical model, and divides the photons in detail to accurately identify and extract information from different parts of the water body. Since surface photons may be affected by factors such as direct sunlight and sea waves, while underwater photons provide actual information inside the water body, dividing photons helps to reduce the interference of surface phenomena on underwater data and improve the accuracy of detection. Then, using the original coordinates of the surface photons for wave fitting reconstruction of sea waves, a photon refraction correction model for the water body is constructed to correct the original coordinates of the underwater photons, correcting the position error caused by water body refraction and improving the accuracy of the underwater photon coordinates, providing guarantee for the detection accuracy in the depth direction. Then, through the underwater profile detection model, non-linear correction is performed on photon radiation transmission to recover the lost photon signals, making the detection data closer to the actual situation. On this basis, combined with satellite image data, a surface chlorophyll a concentration model is established, enabling the detection method to be extended from two-dimensional surface to three-dimensional space, and providing a large range of surface data through satellite images. Finally, by calibrating the initial underwater concentration model and using the calibrated model as the final underwater concentration model, the detection accuracy in the depth direction is further optimized. By combining the surface and underwater concentration models, the three-dimensional chlorophyll a concentration distribution of the regional water body can be accurately provided, not only improving the accuracy and reliability of detection, but also being able to more comprehensively reflect the vertical and horizontal distributions of chlorophyll a in the water body, providing a more accurate and comprehensive data basis for environmental monitoring and scientific research.

[0038] Optionally, the step of dividing the photons of the regional water body into surface photons and underwater photons according to the segmented statistical model includes: Mapping the original coordinates of the photons of the regional water body along the satellite orbit distance to a two-dimensional space to obtain the elevation-orbit distance distribution map of the photons; Slicing the elevation-orbit distance distribution map along the elevation direction to obtain the elevation density histogram of the photons; Obtaining the central elevation of the water surface of the regional water body according to the elevation density histogram; Dividing the photons into the surface photons and the underwater photons according to the central elevation of the water surface.

[0039] Specifically, first, the original coordinates of the photons in the regional water body are mapped along the satellite orbit distance to a two-dimensional space, forming a height - orbit distance distribution map of the photons. This photon dataset is denoted by P. Then, by slicing the height - orbit distance distribution map along the height direction and counting the number of photons in each slice unit, a height density histogram of the photons is obtained, forming the height density histogram. Through the height density histogram, the central height of the water surface in the regional water body can be determined. Finally, the photons are divided into surface photons and underwater photons according to the central height of the water surface.

[0040] In a preferred embodiment of the present invention, as shown in Figure 2 by using a photon - counting lidar to obtain the original data in the water body area, it can be mapped into a two - dimensional space along the orbit distance. This photon dataset is denoted by P: ; where is the along - track distance of the i - th photon signal, is the height of the i - th sub - signal, is the number of photons contained in the original point cloud data, and i is an index variable with a value range from 1 to .

[0041] Taking as the slice interval in the height direction, the original photons are sliced into slice units in the height direction, and the number of photons in each slice unit is counted , forming a height density histogram, as shown in Figure 3 . Among them, the horizontal axis is the central height of each height slice, the vertical axis is the number of photon signals in the slice unit, and the central height value corresponding to the peak is , the central height value corresponding to the peak above it is , and the central height value corresponding to the peak below it is . The purpose is to count the distribution of photon heights and obtain the central height of the water surface . Taking the central positions and as the upper bound, and taking the central positions and as the lower bound, the surface photons and underwater photons are separated to form a surface photon dataset and an underwater photon dataset. In this alternative embodiment, by mapping photon data to a two-dimensional space and performing elevation slicing, a detailed elevation density histogram can be constructed, which helps to identify the distribution characteristics of surface photons and underwater photons. This distinction not only improves the resolution of the data, but also provides a basis for subsequent refraction correction and non-linear correction of radiative transfer, thus significantly improving the accuracy and reliability of the three-dimensional detection of chlorophyll a concentration profiles.

[0042] Optionally, the method for reconstructing the fitting of ocean waves based on the original coordinates of the surface photons to construct a refraction correction model for water body photons includes: Determine the amplitude, angular frequency, direction angle, and wave phase of the ocean wave according to the surface wind speed of the regional water body; Construct an ocean wave model for the regional water body according to the amplitude, angular frequency, direction angle, and wave phase of the ocean wave; Through the ocean wave model, combined with the original coordinates of the surface photons, ray tracing is performed on the original spatial transmission path of each photon in the regional water body to construct a refraction correction model for water body photons.

[0043] Specifically, the key parameters of the ocean wave, including amplitude, angular frequency, direction angle, and wave phase, are determined based on the surface wind speed of the regional water body. These parameters are the basis of the ocean wave model. Then, an ocean wave model for the regional water body is constructed using these parameters. Finally, through the ocean wave model, combined with the original coordinates of the surface photons, ray tracing is performed on the original spatial transmission path of each photon in the regional water body, thereby constructing a refraction correction model for water body photons.

[0044] In a preferred embodiment of the present invention, based on the surface photon dataset , the JONSWAP model is used for high-precision segmented ocean wave fitting reconstruction to obtain an ocean wave model: ; where z(x, y) represents the surface height of the ocean wave at position (x, y); ζ i , ω i , α i and ε i represent the amplitude, angular frequency, direction angle, and wave phase of the ocean wave respectively. These parameters are mainly determined by the sea surface wind speed and are also related to factors such as the peak enhancement factor and wind volume; g and n represent the gravitational acceleration and the number of superimposed cosine waves and sine waves respectively, is a constant offset caused by the negative value of the sea level in the WGS-84 coordinate system, S(ω) represents the ocean wave spectrum, α represents the peak enhancement factor, ω represents the angular frequency of the ocean wave spectrum,ω p represents the peak frequency of the sea wave spectrum, σ represents the width parameter of the sea wave spectrum, and τ represents the duration of the wind or the acting time of the wind.

[0045] In this optional embodiment, by determining the sea wave parameters according to the actual wind speed, the true condition of the water surface is more accurately reflected, which is crucial for constructing a sea wave model. Using this model to perform precise ray tracing on the propagation path of photons, considering the influence of sea waves, a more accurate water body photon refraction correction model is constructed.

[0046] Optionally, correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons includes: Determining the spatial geometric relationships between the water surface slope angle, the incident angle, and the refraction angle of the regional water body and the original spatial transmission path according to the water body photon refraction model; Determining the elevation displacement and the in-track distance displacement of each underwater photon according to the spatial geometric relationships; Correcting the original coordinates of the underwater photons according to the elevation displacement and the in-track distance displacement to obtain the refraction correction coordinates.

[0047] Specifically, first, based on the water body photon refraction model, determine the spatial geometric relationships between the water surface slope angle, the incident angle, and the refraction angle of the regional water body and the original spatial transmission path of the photons. Among them, based on this spatial intersection point, tangent, and the coordinates of the underwater photons, through the spatial structure relationship of water body refraction and the underwater propagation path of photons, perform depth error correction on each water body signal photon according to Snell's law. Then, according to these spatial geometric relationships, determine the elevation displacement and the in-track distance displacement of each underwater photon, and correct the original coordinates of the underwater photons according to these displacements to obtain the refraction correction coordinates, that is, adding the offsets in each direction is the corrected photon coordinates.

[0048] In a preferred embodiment of the present invention, based on the laser incident angle and the coordinates of each underwater photon, construct a spatial line: ; This spatial line is divided into along-track and cross-track directions: ; where x and z represent the coordinate axes in space, x represents the horizontal direction, and z represents the vertical direction; represents the coordinates of the underwater photon p in space, is the position of the photon in the horizontal direction, is the position of the photon in the vertical direction, Denote the components of the unit vector of the spatial line of the underwater photon p in the x and z directions. Denote the straight-line equation in the along-track direction, which is used to describe the projection of the spatial line in the direction perpendicular to the x-axis.

[0049] The slope of the intersection point q of the straight line and the sea surface curve is calculated using the first derivative of the fitted JONSWAP. Denote as follows: ; Based on Snell's law and the original underwater transmission path of the photon p, construct the spatial geometric relationship of water body refraction, combined with Figure 4 as shown in Denote the incident angle, and q denotes the air / sea surface intersection point. In the along-track direction, the point Denote the position of the photon q after refraction by the water body. The spatial geometric relationships on the left and right represent the slope of the intersection point q are greater than or equal to zero and less than zero respectively. In addition, Lx and Rx represent the underwater paths of the photon p with and without considering water body refraction respectively. α and β are the incident angle and the refraction angle at the actual reflection surface respectively. N is the normal of the actual reflection surface.

[0050] In the cases of having and not having water body refraction, the relationships of the underwater photon paths, L and R, the incident angle and the refraction angle, α and β, and the speeds of light in air and water body, Ca and Cw, can be expressed as: ; Where is the refractive index of water, set as 1.3412. For various cases of water body refraction, Ca represents the speed of light in air, Cw represents the speed of light in water body, t represents the propagation time of light in air and water body, α represents the incident angle, that is, the angle between the light entering the water body from air and the normal, β represents the refraction angle, that is, the angle between the light propagating in the water body and the normal, L represents the path length of light in water, and R represents the path length of light in air. Denote the sea surface slope angle, and the sea surface slope angle can be generalized into three ranges: , , and ; is the pointing angle of the laser beam, either greater than or equal to zero or less than zero.

[0051] According to Snell's law, the relationship between the incident angle and the refraction angle can be expressed as: ; Where β represents the refraction angle. Denote the arcsine function. represents the refractive index of water, which is a constant with a value of 1.3412. ɑ represents the angle of incidence, and sinɑ represents the sine value of the angle of incidence.

[0052] Subsequently, through the spatial geometric relationships such as the angle of incidence, the angle of refraction, and the transmission path of underwater photons in the water body, the displacement along the elevation and the displacement along the track distance are respectively expressed as and .

[0053] When , ; ; When , ; ; When , ; ; Among them, the original coordinates of the underwater photons plus the displacement along the elevation and the displacement along the track distance in each direction are the corrected photon coordinates.

[0054] In this optional embodiment, by determining the spatial geometric relationships between the water surface slope angle, the angle of incidence, and the angle of refraction and the original spatial transmission path of the photons, the propagation path of the photons in the water body can be more accurately simulated, taking into account the influence of water refraction. This precise geometric analysis and displacement calculation enable us to effectively correct the original coordinates of the photons, thereby obtaining more accurate refraction correction coordinates.

[0055] Optionally, the non-linear correction of the photon radiation transmission of the water body in the area through the underwater profile detection model to obtain the volume scattering coefficient of the water body in the area at different depths includes: Through the underwater profile detection model, iterative correction of the underwater photons is performed to obtain the actual photon number sequence of the water body in the area at different depths; According to the original coordinates of the underwater photons, the depth of the underwater photons is determined, and according to the depth, combined with the preset radar parameters and water body parameters, the expected photon number sequence of the water body in the area at different depths is determined; According to the actual photon number sequence and the expected photon number sequence of the water body in the area at different depths, the volume scattering coefficient of the water body in the area at different depths is determined.

[0056] Specifically, first, the underwater photons are iteratively corrected through an underwater profile detection model, which involves using the RLD (Richardson-Lucy Deconvolution) algorithm to correct the non-linearity of radiation transmission caused by the after-pulse effect. Through this iterative correction, the actual photon number sequence of the regional water body at different depths can be obtained. Then, the depth of the photon is determined according to the original coordinates of the underwater photon, and combined with the preset radar parameters and water body parameters, the expected photon number sequence of the regional water body at different depths is determined. Finally, by comparing the actual photon number sequence and the expected photon number sequence, we can determine the volume scattering coefficient of the regional water body at different depths.

[0057] In a preferred embodiment of the present invention, as shown in Figure 5 , the original photons are segmented at intervals of 4 km along the orbital direction. For the underwater photons in each segment, they are divided at intervals of 1 m in depth and a step size of 0.15 m in the vertical direction. The number of photons in each 4 km × 1 m frame is counted and divided by the total number of laser emissions, and the average number of underwater photons at different depths z can be calculated. Using a similar cumulative method, the average number of surface signal photons can be calculated. After that, the RLD (Richardson-Lucy Deconvolution) algorithm is used to correct the non-linearity of radiation transmission caused by the after-pulse effect, and the actual photon number sequence at each depth is iteratively restored. The k-th iteration process is: = ; where is the convolution operation, is the number sequence of underwater photons (before correction) in the k-th iteration, is the actual number sequence of underwater photons (after correction) in the k-th iteration, is the average number sequence of the laser captured by the ICESat-2 satellite at different depths, is the impulse response function of ICESat-2. When is less than the preset threshold, the iteration ends.

[0058] Finally, the volume scattering coefficient at different depths is calculated . In unit optical length, the expression of the expected number of underwater photons at a given depth is: ; Among them, z is the water depth, that is, the depth of the underwater photon is determined according to the original coordinates of the underwater photon; β(π, z) is the volume scattering coefficient when the scattering angle is π; α(z) is the lidar attenuation coefficient; the preset lidar parameters include: η is the comprehensive efficiency of the ICESat-2 satellite; is the transmitted laser energy; is the effective area of the receiving telescope; R is the flying altitude of the spaceborne lidar; is the Planck constant; v is the photon frequency; θ is the refraction angle when the laser pulse enters the water column; is the refractive index of the water column; is the one-way atmospheric transmittance; is the one-way water surface transmittance; F is the system calibration factor of ICESat-2; ∆z is half of the depth accumulation interval.

[0059] The expression for the number of photons reflected by the water surface is: ; where ρs is the water surface reflection coefficient; in the open sea area, the root mean square slope of the water surface can be calculated based on the wind speed U10 (wind speed at a height of 10 m above the water surface) ; Combining the above expressions for the expected number of underwater photons corresponding to the given depth and the expression for the number of photons reflected by the water surface, a combined expression can be obtained: ; Among them, A is a coefficient containing lidar system and environmental parameters, which can be approximated as , which is jointly determined by the hardware parameters during acquisition and the corresponding environmental parameters. The lidar attenuation coefficient α can be obtained under the assumption that the lidar underwater signal shows a fixed exponential attenuation. According to the combined expression, the volume scattering coefficient at different depths can be calculated .

[0060] In this optional embodiment, through nonlinear correction, we can accurately correct the nonlinear error in photon radiation transmission and thereby obtain the volume scattering coefficient of the water body at different depths. Recover a photon number sequence closer to the real situation from the actual photon counting data, so it is crucial for improving the accuracy of three-dimensional detection of chlorophyll a concentration profiles.

[0061] Optionally, establishing the initial underwater concentration model of the regional water body according to the refraction-corrected coordinates of the underwater photons and the volume scattering coefficient of the regional water body includes: Determine the corrected depth of the underwater photon according to the refraction-corrected coordinates of the underwater photon; Based on the corrected depth, determine the mathematical model among the corrected depth, the reflection angle corresponding to the corrected depth, and the volume scattering coefficient; Based on the mathematical model and the wavelength correlation coefficient of the laser wavelength, establish the initial underwater concentration model.

[0062] Specifically, calculate the profile chlorophyll a concentration. For nearly isotropic backscattering, the backscattering coefficient of the water body can be expressed as , denotes the backscattering coefficient of the water body at depth z considering the laser wavelength , is expressed as the sum of the backscattering of the water body and the backscattering of the particles . The wavelength is 532 nm used by ICESat-2. Regardless of the laser wavelength, in open water, the backscattering of the particles increases regularly with the increase of the chlorophyll a concentration . Taking the empirical model between and as the initial underwater concentration model, it can be expressed as: ; where and are wavelength correlation coefficients.

[0063] In this alternative embodiment, by accurately determining the depth and scattering characteristics of photons, we can more accurately infer the distribution of chlorophyll a in the water body. This method improves the accuracy and reliability of the three-dimensional detection of the chlorophyll a concentration profile because it is based on actual photon data and physical models, rather than relying on simplified assumptions or indirect measurements. Such a model provides a more accurate tool for water body environmental monitoring and ecological assessment.

[0064] Optionally, modeling the surface chlorophyll a concentration of the water body in the region according to the image bands of the satellite image data to obtain the surface chlorophyll a concentration model of the water body in the region, including: Preprocess the satellite image data, and perform band extraction on the preprocessed satellite image data to obtain band images corresponding to the blue band and the green band; Determine the surface chlorophyll a concentration model according to the band images corresponding to the blue band and the green band, in combination with the remote sensing reflectance value of the satellite image data.

[0065] Specifically, first, preprocess the satellite image data. This step includes atmospheric correction and flare elimination to ensure the accuracy and availability of the data. After preprocessing, extract the band images corresponding to the blue band (e.g., 473 nm) and the green band (e.g., 532 nm) from the satellite image data. These two bands are crucial for the inversion of chlorophyll a concentration. In a preferred embodiment of the present invention, perform atmospheric correction and flare elimination on the multispectral satellite image, and then select the blue and green bands of the image. Based on their remote sensing reflectance values, construct an inversion model for the chlorophyll a concentration in the water surface as the chlorophyll a concentration model in the water surface to accurately extract the chlorophyll a concentration information in the water surface.

[0066] In a preferred embodiment of the present invention, the chlorophyll a concentration model in the water surface is expressed as; ; Where, Chla is the chlorophyll a concentration value, and lg represents the logarithmic operation, is the remote sensing reflectance of the water surface at the blue band, i.e., at a wavelength of 473 nm, is the remote sensing reflectance of the water surface at the green band, i.e., at a wavelength of 532 nm, and the coefficient takes different values according to the specific sensor.

[0067] In this alternative embodiment, through preprocessing and band extraction, the interference of factors such as the atmosphere and light is removed, thereby obtaining more pure and accurate remote sensing data. Combine the remote sensing reflectance values of the blue and green bands to determine the chlorophyll a concentration model in the water surface, and use spectral characteristics to quantitatively analyze the concentration of chlorophyll a, which not only improves the accuracy of chlorophyll a concentration monitoring but also expands the monitoring range, and can quickly evaluate the chlorophyll a concentration of large-scale water bodies.

[0068] Optionally, calibrating the initial underwater concentration model according to the image bands of the satellite image data, and using the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body includes: Determine the chlorophyll a concentration in the water surface according to the chlorophyll a concentration model in the water surface; Determine the water surface particulate scattering coefficient according to the image bands of the satellite image data; Input the chlorophyll a concentration in the water surface and the water surface particulate scattering coefficient into the initial underwater concentration model to correct the wavelength-related coefficient and obtain the corrected wavelength-related coefficient; Calibrate the initial underwater concentration model according to the corrected wavelength-related coefficient, and use the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body.

[0069] Specifically, the surface chlorophyll a concentration and the surface particulate scattering coefficient are input into the initial underwater concentration model to correct the wavelength - related coefficient, and the corrected wavelength - related coefficient is obtained. Since the wavelength - related coefficient directly affects the propagation characteristics of photons in water and the accuracy of concentration inversion. In the preferred embodiment of the present method, the surface particulate scattering coefficient and the surface chlorophyll a concentration are substituted into the relational expression of the above - mentioned initial underwater concentration model to calculate the corrected wavelength - related coefficient and . The vertical distribution of chlorophyll a concentration is calculated based on the particulate scattering coefficient on the depth profile, realizing more accurate detection of the chlorophyll a concentration in the water body profile.

[0070] In this alternative embodiment, by combining the surface chlorophyll a concentration and the surface particulate scattering coefficient, the optical properties of the water body can be more comprehensively understood, and the initial model can be effectively corrected. This calibration process not only improves the accuracy of the model but also enhances the ability to invert the chlorophyll a concentration in the water body.

[0071] Optionally, obtaining the three - dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model includes: Determining the surface chlorophyll a concentration of the regional water body on the water surface according to the surface chlorophyll a concentration model; Determining the underwater chlorophyll a concentration of the regional water body in the vertical profile according to the final underwater concentration model; Taking the surface chlorophyll a concentration and the underwater chlorophyll a concentration as the three - dimensional chlorophyll a concentration of the regional water body.

[0072] Specifically, the chlorophyll a concentration of the regional water body on the water surface is determined according to the surface chlorophyll a concentration model. The satellite image data and remote sensing reflectance values are utilized, and through pre - processing and band extraction, the surface chlorophyll a concentration information is extracted with high precision. Then, the underwater chlorophyll a concentration of the regional water body in the vertical profile is determined according to the final underwater concentration model. This final model is obtained by correcting the wavelength - related coefficient on the basis of the initial underwater concentration model.

[0073] In a preferred embodiment of the present invention, a random forest classification model can be trained to detect the three-dimensional chlorophyll a concentration. The photon sounding data and the multispectral image are matched to the same coordinate system. Since the pixels of the remote sensing image are discrete, while the water depth curve is continuous, the lengths and positions of the water depth curves contained in different pixels are different. Therefore, the water depth value corresponding to each pixel can be calculated by taking the weighted distance from the pixel center to the water depth curve segment in the pixel, overcoming the difference between discrete data and continuous data, and realizing the effective matching of the photon sounding data and the multispectral image. The coupling relationship of the spectral information of the image pixel, the photon water depth information, and the chlorophyll a concentration information at the corresponding depth is constructed. According to the correlation mechanism and interaction between the information of each element, the pixel spectral sample data and the chlorophyll a concentration label data on the depth sequence are established, providing a data basis for training the random forest classification model. After the random forest classification model is trained, the spectral and water depth information on the depth sequence of the target research area is used as the input of the classification model to predict the chlorophyll a concentration value corresponding to the pixel at different water depths, realizing the output of the three-dimensional data of the chlorophyll a concentration area. Combining Figure 6 As shown, first, the spectral image pixels and the water depth data are combined through coordinate matching to perform the calculation of the water depth corresponding to the pixel, specifically including the calculation of the spectral image pixels, the photon water depth curve, and the weighted distance. Then, the element correlation relationship is constructed, and the water depth information and the chlorophyll a concentration information are combined through the chlorophyll a concentration change curve of the depth profile. Then, the water depth data, the water body parameters, the spectral image, and the lidar parameters are used as input data to train the classification model; so that this model can predict the chlorophyll a concentration at different underwater depths according to the input data. Finally, the trained classification model is applied to the target research area to generate the three-dimensional detection result of the chlorophyll a concentration, and this result shows the three-dimensional distribution information of the chlorophyll a concentration in the water body in the form of a three-dimensional graph.

[0074] In this alternative embodiment, by combining the concentration data on the water surface and underwater, a three-dimensional and detailed chlorophyll a concentration distribution map is constructed, breaking through the traditional two-dimensional detection limit, realizing continuous monitoring from the water surface to underwater, and providing a more accurate and comprehensive data basis.

[0075] Combining Figure 7 As shown, the present invention also provides a three-dimensional detection system for chlorophyll a concentration profile, including: A data acquisition unit for acquiring satellite image data and water body photon data of the regional water body, wherein the water body photon data includes the original coordinates of the photons in the preset point cloud coordinate system, and the photons of the regional water body are divided into surface photons and underwater photons according to the segmented statistical model; A refraction correction unit, configured to perform wave fitting reconstruction based on the original coordinates of the water surface photons, construct a water body photon refraction correction model, and correct the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons; A non-linear correction unit, configured to perform non-linear correction on the photon radiation transmission of the regional water body through an underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths; A first model establishment unit, configured to establish an initial underwater concentration model of the regional water body according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the regional water body; A second model establishment unit, configured to model the surface chlorophyll a concentration of the regional water body according to the image band of the satellite image data to obtain a surface chlorophyll a concentration model of the regional water body; A model calibration unit, configured to calibrate the initial underwater concentration model according to the image band of the satellite image data, and use the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; A concentration output unit, configured to obtain the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model.

[0076] The three-dimensional detection system for chlorophyll a concentration profile of the present invention divides photon data into surface photons and underwater photons through a segmented statistical model, and carefully divides the photons to accurately identify and extract information from different parts of the water body. Since surface photons may be affected by factors such as direct sunlight and waves, while underwater photons provide the actual information inside the water body, dividing the photons helps to reduce the interference of surface phenomena on underwater data and improve the accuracy of detection. Then, the original coordinates of the surface photons are used for wave fitting reconstruction of ocean waves, a photon refraction correction model for the water body is constructed, and the original coordinates of the underwater photons are corrected, correcting the position error caused by water body refraction and improving the accuracy of the underwater photon coordinates, providing guarantee for the detection accuracy in the depth direction. Then, the photon radiation transmission is non-linearly corrected through the underwater profile detection model to recover the lost photon signal, making the detection data closer to the actual situation. On this basis, combined with satellite image data, a surface chlorophyll a concentration model is established, enabling the detection method to be extended from a two-dimensional surface to a three-dimensional space, and providing a large range of surface data through satellite images. Finally, by calibrating the initial underwater concentration model and using the calibrated model as the final underwater concentration model, the detection accuracy in the depth direction is further optimized. By combining the surface and underwater concentration models, the three-dimensional chlorophyll a concentration distribution of the regional water body can be accurately provided. The present invention not only improves the accuracy and reliability of detection, but also can more comprehensively reflect the vertical and horizontal distributions of chlorophyll a in the water body, providing a more accurate and comprehensive data basis for environmental monitoring and scientific research.

[0077] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A three-dimensional detection method for chlorophyll a concentration profile, characterized in that Including: Obtaining satellite image data and water body photon data of a region, where the water body photon data includes the original coordinates of photons in a preset point cloud coordinate system, and dividing the photons of the water body in the region into surface photons and underwater photons according to a segmented statistical model; Performing a fitting reconstruction of ocean waves based on the original coordinates of the surface photons, constructing a water body photon refraction correction model, and correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons; Performing a non-linear correction on the photon radiative transfer of the water body in the region through an underwater profile detection model to obtain the volume scattering coefficient of the water body in the region at different depths; Establishing an initial underwater concentration model of the water body in the region according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the water body in the region; Modeling the surface chlorophyll a concentration of the water body in the region according to the image band of the satellite image data to obtain a surface chlorophyll a concentration model of the water body in the region; Calibrating the initial underwater concentration model according to the image band of the satellite image data, and using the calibrated initial underwater concentration model as the final underwater concentration model of the water body in the region; Obtaining the three-dimensional chlorophyll a concentration of the water body in the region according to the surface chlorophyll a concentration model and the final underwater concentration model.

2. The three-dimensional detection method for chlorophyll a concentration profile according to claim 1, wherein The dividing the photons of the water body in the region into surface photons and underwater photons according to the segmented statistical model includes: Mapping the original coordinates of the photons of the water body in the region along the satellite orbit distance to a two-dimensional space to obtain an elevation-orbit distance distribution map of the photons; Slicing the elevation-orbit distance distribution map along the elevation direction to obtain an elevation density histogram of the photons; Obtaining the central elevation of the water surface of the water body in the region according to the elevation density histogram; Dividing the photons into the surface photons and the underwater photons according to the central elevation of the water surface.

3. The three-dimensional detection method for chlorophyll a concentration profile according to claim 1, wherein The performing a fitting reconstruction of ocean waves based on the original coordinates of the surface photons and constructing a water body photon refraction correction model includes: Determining the amplitude, angular frequency, direction angle, and wave phase of the ocean waves according to the surface wind speed of the water body in the region; Constructing an ocean wave model of the water body in the region according to the amplitude, angular frequency, direction angle, and wave phase of the ocean waves; Through the ocean wave model, combining the original coordinates of the surface photons to perform ray tracing on the original spatial transmission path of each photon in the water body in the region to construct a water body photon refraction correction model.

4. The three-dimensional detection method for chlorophyll a concentration profile according to claim 3, wherein The correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons includes: Determining the spatial geometric relationships between the surface slope angle, incident angle, and refraction angle of the water body in the region and the original spatial transmission path according to the water body photon refraction model; Determining the elevation displacement and orbit distance displacement of each underwater photon according to the spatial geometric relationships; Correcting the original coordinates of the underwater photons according to the elevation displacement and orbit distance displacement to obtain the refraction correction coordinates.

5. The three-dimensional detection method for chlorophyll a concentration profile according to claim 1, characterized in that, Performing non-linear correction on the photon radiation transmission of the water body in the region through the underwater profile detection model to obtain the volume scattering coefficient of the water body in the region at different depths, including: Performing iterative correction on the underwater photons through the underwater profile detection model to obtain the actual photon number sequence of the water body in the region at different depths; Determining the depth of the underwater photons according to the original coordinates of the underwater photons, and determining the expected photon number sequence of the water body in the region at different depths according to the depth in combination with preset radar parameters and water body parameters; Determining the volume scattering coefficient of the water body in the region at different depths according to the actual photon number sequence and the expected photon number sequence of the water body in the region at different depths.

6. The three-dimensional detection method for chlorophyll a concentration profile according to claim 1, characterized in that, Establishing the initial underwater concentration model of the water body in the region according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the water body in the region, including: Determining the correction depth of the underwater photons according to the refraction correction coordinates of the underwater photons; Based on the correction depth, determining the mathematical model among the correction depth, the reflection angle corresponding to the correction depth, and the volume scattering coefficient; Establishing the initial underwater concentration model according to the mathematical model in combination with the wavelength correlation coefficient of the laser wavelength.

7. The three-dimensional detection method for chlorophyll a concentration profile according to claim 6, wherein Modeling the chlorophyll a concentration on the water surface of the water body in the region according to the image band of the satellite image data to obtain the chlorophyll a concentration model on the water surface of the water body in the region, including: Preprocessing the satellite image data, and performing band extraction on the preprocessed satellite image data to obtain the band images corresponding to the blue band and the green band; Determining the chlorophyll a concentration model on the water surface according to the band images corresponding to the blue band and the green band in combination with the remote sensing reflectance value of the satellite image data.

8. The three-dimensional detection method for chlorophyll a concentration profile according to claim 7, characterized in that Calibrating the initial underwater concentration model according to the image band of the satellite image data, and taking the calibrated initial underwater concentration model as the final underwater concentration model of the water body in the region, including: Determining the chlorophyll a concentration on the water surface according to the chlorophyll a concentration model on the water surface; Determining the water surface particulate scattering coefficient according to the image band of the satellite image data; Inputting the chlorophyll a concentration on the water surface and the water surface particulate scattering coefficient into the initial underwater concentration model to correct the wavelength correlation coefficient and obtain the corrected wavelength correlation coefficient; Calibrating the initial underwater concentration model according to the corrected wavelength correlation coefficient, and taking the calibrated initial underwater concentration model as the final underwater concentration model of the water body in the region.

9. The three-dimensional detection method for chlorophyll a concentration profile according to claim 8, characterized in that, Obtaining the three-dimensional chlorophyll a concentration of the water body in the region according to the chlorophyll a concentration model on the water surface and the final underwater concentration model, including: Determining the chlorophyll a concentration on the water surface of the water body in the region according to the chlorophyll a concentration model on the water surface; Determining the underwater chlorophyll a concentration of the water body in the vertical profile according to the final underwater concentration model; Taking the chlorophyll a concentration on the water surface and the underwater chlorophyll a concentration as the three-dimensional chlorophyll a concentration of the water body in the region.

10. A three-dimensional detection system for chlorophyll a concentration profiles, characterized in that, Including: A data acquisition unit for acquiring satellite image data and water body photon data of a regional water body, wherein the water body photon data includes the original coordinates of photons in a preset point cloud coordinate system, and divides the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model; A refraction correction unit for performing wave fitting reconstruction based on the original coordinates of the surface photons, constructing a water body photon refraction correction model, and correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction correction coordinates of the underwater photons; A non-linear correction unit for performing non-linear correction on the photon radiation transmission of the regional water body through an underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths; A first model establishment unit for establishing an initial underwater concentration model of the regional water body according to the refraction correction coordinates of the underwater photons and the volume scattering coefficient of the regional water body; A second model establishment unit for modeling the surface chlorophyll a concentration of the regional water body according to the image band of the satellite image data to obtain a surface chlorophyll a concentration model of the regional water body; A model calibration unit for calibrating the initial underwater concentration model according to the image band of the satellite image data, and using the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; A concentration output unit for obtaining the three-dimensional chlorophyll a concentration of the regional water body according to the surface chlorophyll a concentration model and the final underwater concentration model.

Images