A three-dimensional detection method and system for chlorophyll a concentration profile

Abstract

The present invention provides a three-dimensional chlorophyll-a concentration profile detection method and system, which relates to the field of remote sensing data processing technology. The method comprises obtaining satellite image data and water photon data of a regional water body, dividing the photons of the regional water body into surface photons and underwater photons, performing ocean wave fitting reconstruction, and constructing a water body photon refraction correction model. The underwater photon coordinates are corrected to obtain refraction-corrected coordinates. Using the underwater profile detection model, nonlinear correction is performed on the photon radiation transmission to obtain a volume scattering coefficient. The refraction-corrected coordinates and the volume scattering coefficient are combined to establish an underwater concentration model. The water surface chlorophyll-a concentration is modeled using satellite image bands, and the underwater concentration model is calibrated to obtain a final underwater concentration model. Finally, the surface and underwater chlorophyll-a concentration models are combined to obtain the three-dimensional chlorophyll-a concentration of the regional water body. The present invention not only improves the accuracy and reliability of detection, but also can reflect the vertical and horizontal distribution of chlorophyll-a in the water body.

Description

Technical Field

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

[0002] Currently, chlorophyll-a concentration, as a key parameter reflecting the ecological health of water bodies, is widely used in fields such as marine monitoring, environmental protection, and resource management. Furthermore, changes in chlorophyll-a concentration often directly affect the balance of local ecosystems. Therefore, accurate detection of chlorophyll-a concentration in regional water bodies is of great significance for environmental monitoring and scientific research.

[0003] In the existing technology, the means of detecting chlorophyll a concentration generally adopts satellite remote sensing or on-site sampling. Both of the above methods mainly provide two-dimensional surface data of chlorophyll a concentration, which makes it difficult to effectively obtain vertical distribution information within the water body. In addition, due to the dynamic complexity of the water body, the detection accuracy and 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, comprising:

[0007] Acquire satellite image data and water body photon data of a regional water body, wherein the water body photon data includes 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;

[0008] Performing wave fitting reconstruction based on the original coordinates of the water surface photons to construct 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;

[0009] Using an underwater profile detection model, nonlinear correction is performed on the photon radiation transmission of the water body in the region to obtain the volume scattering coefficient of the water body in the region at different depths;

[0010] Establishing an initial underwater concentration model of the water body in the region according to the refraction-corrected coordinates of the underwater photons and the volume scattering coefficient of the water body in the region;

[0011] Modeling the chlorophyll a concentration of the surface of the water body in the region according to the image bands of the satellite image data to obtain a chlorophyll a concentration model of the surface of the water body in the region;

[0012] 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;

[0013] The three-dimensional chlorophyll a concentration of the water body in the region is obtained according to the water surface chlorophyll a concentration model and the final underwater concentration model.

[0014] Optionally, dividing the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model includes:

[0015] Mapping the original coordinates of the photons of the water body in the region into a two-dimensional space along the satellite orbit distance to obtain an elevation-orbit distance distribution map of the photons;

[0016] Slicing the elevation-track distance distribution graph along the elevation direction to obtain an elevation density histogram of the photons;

[0017] Obtaining the center elevation of the water surface of the water body in the region according to the elevation density histogram;

[0018] The photons are divided into the water surface photons and the underwater photons according to the water surface center elevation.

[0019] Optionally, performing ocean wave fitting reconstruction based on the original coordinates of the water surface photons to construct a water body photon refraction correction model includes:

[0020] determining the amplitude, angular frequency, direction angle, and wave phase of ocean waves based on the surface wind speed of the water body in the region;

[0021] 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 wave;

[0022] The ocean wave model is used to trace the original spatial transmission path of each photon in the water body in the region in combination with the original coordinates of the water surface photons to construct a water body photon refraction correction model.

[0023] Optionally, correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction-corrected coordinates of the underwater photons includes:

[0024] Determining, based on the water body photon refraction model, the spatial geometric relationships between the water surface slope angle, the incident angle, and the refraction angle of the water body in the region and the original spatial transmission path;

[0025] Determining the elevation displacement and the distance displacement along the track of each underwater photon according to the spatial geometric relationship;

[0026] The original coordinates of the underwater photons are corrected according to the elevation displacement and the distance displacement along the track to obtain the refraction-corrected coordinates.

[0027] Optionally, performing nonlinear correction on the photon radiation transmission of the regional water body through the underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths includes:

[0028] By using the underwater profile detection model, the underwater photons are iteratively corrected to obtain a sequence of actual photon quantities at different depths in the water body of the region;

[0029] Determine the depth of the underwater photons according to the original coordinates of the underwater photons, and determine the expected number sequence of photons at different depths of the regional water body according to the depth in combination with preset radar parameters and water body parameters;

[0030] The volume scattering coefficients of the regional water body at different depths are determined according to the actual photon number sequence and the expected photon number sequence of the regional water body at different depths.

[0031] Optionally, establishing an 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:

[0032] determining a correction depth of the underwater photon according to the refraction correction coordinates of the underwater photon;

[0033] Determining, based on the correction depth, a mathematical model between the correction depth, a reflection angle corresponding to the correction depth, and the volume scattering coefficient;

[0034] The initial underwater concentration model is established based on the mathematical model combined with the wavelength correlation coefficient of the laser wavelength.

[0035] 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 includes:

[0036] Preprocessing the satellite image data, and performing band extraction on the preprocessed satellite image data to obtain band images corresponding to the blue band and the green band;

[0037] The water surface chlorophyll a concentration model is determined based on the band images corresponding to the blue band and the green band and combined with the remote sensing reflectance value of the satellite image data.

[0038] 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:

[0039] determining the water surface chlorophyll a concentration according to the water surface chlorophyll a concentration model;

[0040] determining a water surface particle scattering coefficient according to an image band of the satellite image data;

[0041] Inputting the water surface chlorophyll a concentration and the water surface particle scattering coefficient into the initial underwater concentration model, and correcting the wavelength correlation coefficient to obtain a corrected wavelength correlation coefficient;

[0042] The initial underwater concentration model is calibrated according to the correction wavelength correlation coefficient, and the calibrated initial underwater concentration model is used as the final underwater concentration model of the regional water body.

[0043] Optionally, obtaining the three-dimensional chlorophyll a concentration of the regional water body according to the water surface chlorophyll a concentration model and the final underwater concentration model includes:

[0044] determining the surface chlorophyll a concentration of the water body in the region according to the water surface chlorophyll a concentration model;

[0045] Determining the underwater chlorophyll a concentration of the regional water body in a vertical section according to the final underwater concentration model;

[0046] The water surface chlorophyll a concentration and the underwater chlorophyll a concentration are used as the three-dimensional chlorophyll a concentration of the water body in the region.

[0047] In a second aspect, the present invention provides a three-dimensional detection system for chlorophyll a concentration profile, comprising:

[0048] 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 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;

[0049] a refraction correction unit, configured to perform sea 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;

[0050] a nonlinear correction unit, configured to perform nonlinear correction on the photon radiation transmission of the water body in the region by using an underwater profile detection model, and obtain a volume scattering coefficient of the water body in the region at different depths;

[0051] A first model building unit is configured to build 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;

[0052] A second model building unit is configured to model the chlorophyll a concentration of the water surface of the water body in the region according to the image band of the satellite image data, so as to obtain a chlorophyll a concentration model of the water surface of the water body in the region;

[0053] a model correction unit, configured to 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;

[0054] The concentration output unit is used to obtain the three-dimensional chlorophyll a concentration of the water body in the area according to the water surface chlorophyll a concentration model and the final underwater concentration model.

[0055] The present invention's three-dimensional chlorophyll a concentration profile detection method and system uses a segmented statistical model to divide photon data into surface photons and underwater photons. This detailed segmentation allows accurate identification and extraction of information from different parts of the water body. Because surface photons may be affected by factors such as direct sunlight and waves, while underwater photons provide actual information about the water's interior, segmenting photons helps reduce interference from surface phenomena on underwater data, thereby improving detection accuracy. Next, the original coordinates of the surface photons are reconstructed using wave fitting, and a water photon refraction correction model is constructed to correct the original coordinates of the underwater photons. This corrects for positional errors caused by water refraction, improves the accuracy of the underwater photon coordinates, and ensures detection accuracy in the depth direction. The underwater profile detection model then performs nonlinear correction on photon radiation transmission, recovering lost photon signals and making the detection data closer to the actual situation. Furthermore, a surface chlorophyll a concentration model is established in conjunction with satellite imagery data, enabling the detection method to be expanded from a two-dimensional surface to a three-dimensional space, providing a wide range of surface data through satellite imagery. Finally, the initial underwater concentration model is calibrated and used as the final underwater concentration model, further optimizing depth-direction detection accuracy. By combining surface and underwater concentration models, the method accurately provides a three-dimensional chlorophyll-a concentration profile for a given area. This not only improves detection accuracy and reliability, but also more comprehensively reflects the vertical and horizontal distribution of chlorophyll-a in the water. This increases the spatial coverage of three-dimensional chlorophyll-a concentration profile detection, providing a more accurate and comprehensive data foundation for environmental monitoring and scientific research. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of a three-dimensional detection method for chlorophyll a concentration profile according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of raw data collected by a photon counting laser radar according to an embodiment of the present invention;

[0058] Figure 3 Schematic diagram of an elevation density histogram according to an embodiment of the present invention;

[0059] Figure 4 Schematic diagram of the spatial structure of displacement error caused by water refraction according to an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram of counting the number of photons in an underwater sliding window according to an embodiment of the present invention;

[0061] Figure 6 This is a schematic diagram of three-dimensional detection of chlorophyll a concentration using active and passive data fusion according to an embodiment of the present invention;

[0062] Figure 7 4 is a structural block diagram of a three-dimensional detection system for chlorophyll a concentration profile according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0064] It should be understood that the various steps described in the method embodiments of the present invention may be performed 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 respect.

[0065] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other 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 of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0066] 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 otherwise clearly indicated in the context, it should be understood as "one or more".

[0067] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0068] like Figure 1 As shown, an embodiment of the present invention provides a three-dimensional detection method for chlorophyll a concentration profile, comprising:

[0069] Satellite image data and water body photon data of regional water bodies are obtained, wherein the water body photon data includes the original coordinates of the photons in a preset point cloud coordinate system, and the photons of the regional water bodies are divided into surface photons and underwater photons according to a segmented statistical model.

[0070] Specifically, the system first collects satellite imagery and photon data of the water body. The satellite imagery provides macroscopic information about the water surface, while the photon data, containing the original coordinates of photons in a preset point cloud coordinate system, provides microscopic information about the water's interior. Based on a segmented statistical model, the photon data is further divided into surface photons and underwater photons, which facilitates more accurate identification and extraction of surface and underwater photon signals.

[0071] Based on the original coordinates of the water surface photons, 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.

[0072] Specifically, the original coordinates of the water surface photons are used to fit and reconstruct the ocean wave. In a preferred embodiment of the present invention, an ocean wave spectrum model such as the JONSWAP model can be used to simulate the characteristics of ocean waves. Based on the water surface photon dataset, the JONSWAP model is used to perform high-precision segmented ocean wave fitting and reconstruction. Through this model, a water body photon refraction correction model can be constructed.

[0073] The underwater profile detection model is used to perform nonlinear correction on the photon radiation transmission of the water body in the region to obtain the volume scattering coefficient of the water body in the region at different depths.

[0074] Specifically, a water photon refraction model based on the wave spectrum model is constructed through water surface signal photons to realize the coordinate correction of water photons and obtain the refraction-corrected coordinates, thereby correcting the position deviation caused by refraction when photons propagate in the water body.

[0075] An initial underwater concentration model of the water body in the region is established according to the refraction-corrected coordinates of the underwater photons and the volume scattering coefficient of the water body in the region.

[0076] Specifically, an underwater profile detection model is used to perform nonlinear correction on photon radiation transmission. In a preferred embodiment of the present invention, a technique such as the Richardson-Lucy Deconvolution algorithm can be used to correct the radiation transmission nonlinearity caused by the after-pulse effect to restore the radiation transmission nonlinearity caused by the after-pulse effect.

[0077] The surface chlorophyll a concentration of the water body in the region is modeled according to the image bands of the satellite image data to obtain a surface chlorophyll a concentration model of the water body in the region.

[0078] Specifically, the chlorophyll-a concentration of regional water bodies was modeled based on the image bands of satellite imagery data. Multispectral satellite imagery was used to assist in the calibration of the chlorophyll-a concentration parameter on the water surface, thereby combining satellite imagery data with photon data to improve detection accuracy.

[0079] The initial underwater concentration model is calibrated according to the image band of the satellite image data, and the calibrated initial underwater concentration model is used as the final underwater concentration model of the regional water body.

[0080] Specifically, the initial underwater concentration model was calibrated using the image bands of satellite image data, and the calibrated model was used 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.

[0081] The three-dimensional chlorophyll a concentration of the water body in the region is obtained according to the water surface chlorophyll a concentration model and the final underwater concentration model.

[0082] Specifically, the three-dimensional chlorophyll-a concentration of the regional water body is derived based on the surface chlorophyll-a concentration model and the final underwater concentration model. This step involves aligning the photon bathymetry data and multispectral imagery to the same coordinate system and constructing a classification model to predict the chlorophyll-a concentration values ​​corresponding to pixels at different water depths. This ultimately enables highly accurate and efficient three-dimensional detection of chlorophyll-a concentration in the water body.

[0083] The present invention's three-dimensional chlorophyll a concentration profile detection method uses a segmented statistical model to divide photon data into surface photons and underwater photons. This detailed segmentation allows accurate identification and extraction of information from different parts of the water body. Because surface photons may be affected by factors such as direct sunlight and waves, while underwater photons provide actual information about the water's interior, segmenting photons helps reduce interference from surface phenomena on underwater data, thereby improving detection accuracy. Next, the original coordinates of the surface photons are used to perform wave fitting reconstruction, constructing a water photon refraction correction model to correct the original coordinates of the underwater photons. This corrects positional errors caused by water refraction, improves the accuracy of the underwater photon coordinates, and ensures detection accuracy in the depth direction. The underwater profile detection model then performs nonlinear correction on photon radiation transmission, recovering lost photon signals and making the detection data closer to the actual situation. Furthermore, a surface chlorophyll a concentration model is established in conjunction with satellite imagery data, enabling the detection method to be expanded from a two-dimensional surface to a three-dimensional space, providing a wide range of surface data through satellite imagery. Finally, the initial underwater concentration model was calibrated and used as the final underwater concentration model, further optimizing depth-direction detection accuracy. By combining surface and underwater concentration models, the system accurately provides the three-dimensional chlorophyll-a concentration distribution of regional water bodies. This not only improves detection accuracy and reliability, but also more comprehensively reflects the vertical and horizontal distribution of chlorophyll-a in the water, providing a more accurate and comprehensive data foundation for environmental monitoring and scientific research.

[0084] Optionally, dividing the photons of the regional water body into surface photons and underwater photons according to a segmented statistical model includes:

[0085] Mapping the original coordinates of the photons of the water body in the region into a two-dimensional space along the satellite orbit distance to obtain an elevation-orbit distance distribution map of the photons;

[0086] Slicing the elevation-track distance distribution graph along the elevation direction to obtain an elevation density histogram of the photons;

[0087] Obtaining the center elevation of the water surface of the water body in the region according to the elevation density histogram;

[0088] The photons are divided into the water surface photons and the underwater photons according to the water surface center elevation.

[0089] Specifically, the original coordinates of the photons in the regional water body are first mapped into two-dimensional space along the satellite orbital distance, forming an elevation-orbital distance distribution map of the photons. This photon dataset is denoted by P. Next, the elevation-orbital distance distribution map is sliced ​​along the elevation direction and the number of photons in each slice unit is counted to obtain an elevation density histogram of the photons. The elevation density histogram can be used to determine the center elevation of the regional water body. Finally, the photons are divided into surface photons and underwater photons based on the center elevation of the water surface.

[0090] In a preferred embodiment of the present invention, Figure 2 As shown in Figure 1, the raw data obtained in the water area by photon counting lidar can be mapped into a two-dimensional space using the distance along the track. The photon dataset is represented by P:

[0091] ;

[0092] in, is the distance along the track of the i-th photon signal, is the elevation of the ith sub-signal, is the number of photons contained in the original point cloud data, i is the index variable, and its value range is from 1 to .

[0093] by is the slicing interval in the elevation direction, which divides the original photon into Slice units, count the number of photons in each slice unit , forming an elevation density histogram, such as Figure 3 As shown. Among them, the horizontal axis is the center elevation of each elevation slice, the vertical axis is the number of photon signals in the slice unit, and the center elevation value corresponding to the peak is , the center elevation value of the peak above it is , the corresponding center elevation value of the crest below it is The purpose is to count the distribution of photon elevation and obtain the elevation of the center of the water surface. .by and Central location is the upper bound, and Central location As the lower bound, the surface photons and underwater photons are separated to form the surface photon dataset and the underwater photon dataset.

[0094] In this optional embodiment, by mapping the photon data into two-dimensional space and performing elevation slicing, a detailed elevation density histogram can be constructed, which helps identify the distribution characteristics of surface and underwater photons. This distinction not only improves the data resolution but also provides a basis for subsequent refraction correction and radiation transfer nonlinearity correction, significantly improving the accuracy and reliability of three-dimensional detection of chlorophyll a concentration profiles.

[0095] Optionally, performing ocean wave fitting reconstruction based on the original coordinates of the water surface photons to construct a water body photon refraction correction model includes:

[0096] determining the amplitude, angular frequency, direction angle, and wave phase of ocean waves based on the surface wind speed of the water body in the region;

[0097] 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 wave;

[0098] The ocean wave model is used to trace the original spatial transmission path of each photon in the water body in the region in combination with the original coordinates of the water surface photons to construct a water body photon refraction correction model.

[0099] Specifically, the key parameters of ocean waves, including amplitude, angular frequency, azimuth, and phase, are determined based on the surface wind speed of the regional water body. These parameters form the basis of the ocean wave model. These parameters are then used to construct the ocean wave model for the regional water body. Finally, using the ocean wave model and the original coordinates of the water surface photons, ray tracing is performed on the original spatial transmission path of each photon in the regional water body, thereby constructing a water photon refraction correction model.

[0100] In a preferred embodiment of the present invention, based on the water surface photon dataset , the JONSWAP model is used to perform high-precision segmented ocean wave fitting and reconstruction to obtain the ocean wave model:

[0101] ;

[0102] Where z(x, y): represents the height of the wave surface at the position (x, y); ζ i , ω i , α i and ε i They represent the amplitude, angular frequency, direction angle and wave phase of the ocean wave, respectively. These parameters are mainly determined by the wind speed on the sea surface and are also related to factors such as the peak enhancement factor and wind volume. g and n represent the acceleration of gravity and the number of superimposed cosine waves and sine waves, respectively. is the constant offset caused by the negative sea level in the WGS-84 coordinate system, S(ω) represents the wave spectrum, and α represents the peak enhancement factor. ω represents the angular frequency of the wave spectrum, ω p represents the peak frequency of the wave spectrum, σ represents the width parameter of the wave spectrum, and τ represents the duration of the wind or the time of wind action.

[0103] In this optional embodiment, by determining wave parameters based on actual wind speed, the true conditions of the water surface are more accurately reflected, which is crucial for constructing a wave model. This model is used to accurately trace the propagation paths of photons, taking into account the influence of waves, thereby constructing a more accurate water photon refraction correction model.

[0104] Optionally, correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction-corrected coordinates of the underwater photons includes:

[0105] Determining, based on the water body photon refraction model, the spatial geometric relationships between the water surface slope angle, the incident angle, and the refraction angle of the water body in the region and the original spatial transmission path;

[0106] Determining the elevation displacement and the distance displacement along the track of each underwater photon according to the spatial geometric relationship;

[0107] The original coordinates of the underwater photons are corrected according to the elevation displacement and the distance displacement along the track to obtain the refraction-corrected coordinates.

[0108] Specifically, the spatial geometric relationship between the water surface slope angle, incident angle and refraction angle of the regional water body and the original spatial transmission path of the photon is first determined based on the water photon refraction model. Based on the spatial intersection, tangent and underwater photon coordinates, the depth error of each water body signal photon is corrected according to Snell's law through the spatial structural relationship between water body refraction and the underwater propagation path of the photon. Then, based on these spatial geometric relationships, the elevation displacement and track distance displacement of each underwater photon are determined. According to these displacements, the original coordinates of the underwater photon are corrected to obtain the refraction-corrected coordinates, that is, the corrected photon coordinates are obtained by adding the offset in each direction.

[0109] In a preferred embodiment of the present invention, based on the laser incident angle Construct a spatial line with the coordinates of each underwater photon:

[0110] ;

[0111] The spatial line is divided into along-track and perpendicular-track directions:

[0112] ;

[0113] Among them, x and z represent the coordinate axes in space, x represents the horizontal direction, and z represents the vertical direction; represents the coordinates of underwater photon p in space, is the horizontal position of the photon, is the vertical position of the photon, represents the x- and z-components of the unit vector of the spatial line through the underwater photon p, It represents the equation of a straight line in the vertical direction, and is used to describe the projection of a space line in the direction perpendicular to the x-axis.

[0114] 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. Represented as follows:

[0115] ;

[0116] Based on Snell's law and the original underwater transmission path of photon p, the spatial geometric relationship of water refraction is constructed, combined with Figure 4 As shown, represents the angle of incidence, and q represents the air / sea surface intersection point. In the along-track direction, point Represents the position of photon q after refraction through water. The spatial geometric relationship between the left and right sides represents the slope of the intersection point q are greater than or equal to zero and less than zero, respectively. Furthermore, Lx and Rx represent the underwater path of photon p with and without water refraction, respectively. α and β are the angle of incidence and the angle of refraction at the actual reflecting surface, respectively. N is the normal to the actual reflecting surface.

[0117] The relationship between the underwater photon path, L and R, the angles of incidence and refraction, α and β, and the speed of light in air and water, Ca and Cw, with and without water refraction can be expressed as:

[0118] ;

[0119] in, is the refractive index of water, set to 1.3412. For various cases of water refraction, Ca represents the speed of light in air, Cw represents the speed of light in water, t represents the propagation time of light in air and water, α represents the angle of incidence, that is, the angle between the light and the normal when it enters the water from the air, β represents the angle of refraction, that is, the angle between the light and the normal when it propagates in the water, L represents the path length of light in water, and R represents the path length of light in air. Represents the sea surface slope angle, which can be summarized into three ranges: , ,and ; is the pointing angle of the laser beam, which is either greater than or equal to zero, or less than zero.

[0120] According to Snell's law, the relationship between the angle of incidence and the angle of refraction can be expressed as:

[0121] ;

[0122] Where β represents the refraction angle, represents the inverse sine 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 of the angle of incidence.

[0123] Then, through the spatial geometric relationships such as the incident angle, refraction angle, and the transmission path of the seabed photon in the water, the displacement along the elevation and the displacement along the track are expressed as and .

[0124] when hour,

[0125] ;

[0126] ;

[0127] when hour,

[0128] ;

[0129] ;

[0130] when hour,

[0131] ;

[0132] ;

[0133] Among them, the original coordinates of the underwater photon plus the elevation displacement and track distance displacement in various directions are the corrected photon coordinates.

[0134] In this optional embodiment, by determining the spatial geometric relationship between the water surface slope angle, the angle of incidence, and the angle of refraction, and the photon's original spatial transmission path, we can more accurately simulate the photon's propagation path in water, taking into account the effects of water refraction. This precise geometric analysis and displacement calculation enables us to effectively correct the photon's original coordinates, thereby obtaining more accurate refraction-corrected coordinates.

[0135] Optionally, performing nonlinear correction on the photon radiation transmission of the regional water body through the underwater profile detection model to obtain the volume scattering coefficient of the regional water body at different depths includes:

[0136] By using the underwater profile detection model, the underwater photons are iteratively corrected to obtain a sequence of actual photon quantities at different depths in the water body of the region;

[0137] Determine the depth of the underwater photons according to the original coordinates of the underwater photons, and determine the expected number sequence of photons at different depths of the regional water body according to the depth in combination with preset radar parameters and water body parameters;

[0138] The volume scattering coefficients of the regional water body at different depths are determined according to the actual photon number sequence and the expected photon number sequence of the regional water body at different depths.

[0139] Specifically, underwater photons are first iteratively corrected using an underwater profile detection model. This involves using the RLD (Richardson-Lucy Deconvolution) algorithm to correct for the nonlinearity of radiative transmission caused by the after-pulse effect. This iterative correction allows us to obtain the actual number of photons at different depths in the regional water body. Next, the depth of the photons is determined based on their original coordinates. Combined with preset radar and water parameters, the expected number of photons at different depths in the regional water body is determined. Finally, by comparing the actual and expected photon number sequences, we can determine the volume scattering coefficient of the regional water body at different depths.

[0140] In a preferred embodiment of the present invention, Figure 5 As shown, the original photons are segmented at intervals of 4 km along the track direction. For the underwater photons in each segment, they are divided vertically at intervals of 1 m depth and a step size of 0.15 m. The number of photons in each 4 km × 1 m box is counted and divided by the total number of laser emissions to calculate the average number of underwater photons at different depths z. Using a similar accumulation method, the average number of signal photons on the water surface can be calculated Afterwards, the RLD (Richardson-Lucy Deconvolution) algorithm is used to correct the nonlinearity of radiation transmission caused by the after-pulse effect, and the actual number of photons at each depth is iteratively restored. The k-th iteration process is:

[0141] = ;

[0142] in, is the convolution operation, is the (uncorrected) number sequence of underwater photons in the kth iteration, is the (corrected) actual number of underwater photons in the kth iteration, is the average number sequence of lasers captured by the ICESat-2 satellite at different depths, is the impulse response function of ICESat-2, If the value is less than the preset threshold, the iteration ends.

[0143] Finally, the volume scattering coefficients at different depths are calculated The expression for the expected number of underwater photons per unit light length at a given depth is:

[0144] ;

[0145] Where z is the water depth, that is, the depth of the underwater photon is determined based on 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 radar parameters include: η is the ICESat-2 satellite comprehensive efficiency; is the transmitted laser energy; is the effective area of ​​the receiving telescope; R is the flight altitude of the satellite-borne lidar; is Planck's constant; v is the photon frequency; θ is the refraction angle of the laser pulse when it 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 ICESat-2 system calibration factor; ∆z is half the depth integration interval.

[0146] The expression for the number of photons reflected by the water surface is:

[0147] ;

[0148] in ρs is the water surface reflection coefficient; in open sea areas, the mean square slope of the water surface can be calculated based on the wind speed U10 on the sea surface (the wind speed at a height of 10 m above the water surface): ; Combining the above expression for the expected number of underwater photons corresponding to a given depth with the expression for the number of photons reflected from the water surface, we can obtain the joint expression:

[0149] ;

[0150] Among them, A is a coefficient that includes the lidar system and environmental parameters, which can be approximated as , which is determined by the hardware parameters and the corresponding environmental parameters during acquisition. The radar attenuation coefficient α can be obtained by assuming that the lidar underwater signal decays at a fixed exponential rate. Based on the joint expression, the volume scattering coefficient at different depths can be calculated. .

[0151] In this optional embodiment, nonlinear correction allows us to accurately correct for nonlinear errors in photon radiation transmission and, accordingly, derive the volume scattering coefficient of the water body at different depths. This allows us to recover a photon count sequence that is closer to the actual state from the actual photon counting data, which is crucial for improving the accuracy of three-dimensional detection of chlorophyll a concentration profiles.

[0152] Optionally, establishing an 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:

[0153] determining a correction depth of the underwater photon according to the refraction correction coordinates of the underwater photon;

[0154] Determining, based on the correction depth, a mathematical model between the correction depth, a reflection angle corresponding to the correction depth, and the volume scattering coefficient;

[0155] The initial underwater concentration model is established based on the mathematical model combined with the wavelength correlation coefficient of the laser wavelength.

[0156] Specifically, the chlorophyll a concentration of the profile is calculated. For nearly isotropic backscattering, the backscattering coefficient of the water body is It can be expressed as , represents the backscattering coefficient of the water body at depth z taking into account the laser wavelength , Represented as water backscatter Particle backscatter Sum of wavelengths The 532 nm used by ICESat-2. Regardless of the laser wavelength, in open water, the backscattering of particles As chlorophyll a concentration The rise of and The empirical model between is used as the initial underwater concentration model and can be expressed as:

[0157] ;

[0158] in, and is the wavelength correlation coefficient.

[0159] In this alternative embodiment, by precisely determining the depth and scattering properties of photons, we can more accurately infer the distribution of chlorophyll a in water. This method improves the accuracy and reliability of three-dimensional detection of chlorophyll a concentration profiles because it is based on actual photon data and physical models, rather than relying on simplified assumptions or indirect measurements. Such models provide more precise tools for aquatic environmental monitoring and ecological assessment.

[0160] 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 includes:

[0161] Preprocessing the satellite image data, and performing band extraction on the preprocessed satellite image data to obtain band images corresponding to the blue band and the green band;

[0162] The water surface chlorophyll a concentration model is determined based on the band images corresponding to the blue band and the green band and combined with the remote sensing reflectance value of the satellite image data.

[0163] Specifically, the satellite image data is first preprocessed, including atmospheric correction and flare removal to ensure data accuracy and usability. After preprocessing, band images corresponding to the blue band (e.g., 473 nm) and the green band (e.g., 532 nm) are extracted from the satellite image data. These two bands are crucial for inverting chlorophyll a concentration. In a preferred embodiment of the present invention, the multispectral satellite image is atmospherically corrected and flare removed. Then, the blue and green bands of the image are selected and, based on their remote sensing reflectance values, a water surface chlorophyll a concentration inversion model is constructed as the water surface chlorophyll a concentration model, thereby extracting water surface chlorophyll a concentration information with high precision.

[0164] In a preferred embodiment of the present invention, the water surface chlorophyll a concentration model is expressed as;

[0165] ;

[0166] in, Chla is the chlorophyll a concentration value, lg represents the logarithmic operation, It is the water surface remote sensing reflectivity in the blue band, i.e. the wavelength is 473nm. is the water surface remote sensing reflectivity in the green band, i.e., at a wavelength of 532nm, the coefficient The value varies depending on the specific sensor.

[0167] In this optional embodiment, interference from atmospheric and light factors is removed through preprocessing and band extraction, resulting in purer and more accurate remote sensing data. The water surface chlorophyll a concentration model is determined by combining remote sensing reflectance values ​​from the blue and green bands. Spectral characteristics are then used to quantitatively analyze chlorophyll a concentration. This not only improves the accuracy of chlorophyll a concentration monitoring but also expands the monitoring range, enabling rapid assessment of chlorophyll a concentration across a wide range of water bodies.

[0168] 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:

[0169] determining the water surface chlorophyll a concentration according to the water surface chlorophyll a concentration model;

[0170] determining a water surface particle scattering coefficient according to an image band of the satellite image data;

[0171] Inputting the water surface chlorophyll a concentration and the water surface particle scattering coefficient into the initial underwater concentration model, and correcting the wavelength correlation coefficient to obtain a corrected wavelength correlation coefficient;

[0172] The initial underwater concentration model is calibrated according to the correction wavelength correlation coefficient, and the calibrated initial underwater concentration model is used as the final underwater concentration model of the regional water body.

[0173] Specifically, the water surface chlorophyll a concentration and the water surface particle scattering coefficient are input into the initial underwater concentration model to correct the wavelength correlation coefficient and obtain the corrected wavelength correlation coefficient. Because the wavelength correlation coefficient directly affects the propagation characteristics of photons in water and the accuracy of concentration inversion. In a preferred embodiment of the present invention, the water surface particle scattering coefficient and the water surface chlorophyll a concentration are brought into the relationship formula of the above-mentioned initial underwater concentration model to solve the corrected wavelength correlation coefficient. and The vertical distribution of chlorophyll a concentration is calculated based on the particle scattering coefficient on the depth profile, achieving more accurate detection of chlorophyll a concentration in the water profile.

[0174] In this optional embodiment, by combining the water surface chlorophyll-a concentration and the water surface particle scattering coefficient, a more comprehensive understanding of the optical properties of the water body can be achieved, 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.

[0175] Optionally, obtaining the three-dimensional chlorophyll a concentration of the regional water body according to the water surface chlorophyll a concentration model and the final underwater concentration model includes:

[0176] determining the surface chlorophyll a concentration of the water body in the region according to the water surface chlorophyll a concentration model;

[0177] Determining the underwater chlorophyll a concentration of the regional water body in a vertical section according to the final underwater concentration model;

[0178] The water surface chlorophyll a concentration and the underwater chlorophyll a concentration are used as the three-dimensional chlorophyll a concentration of the water body in the region.

[0179] Specifically, the surface chlorophyll-a concentration of the regional water body was determined based on a surface chlorophyll-a concentration model. Using satellite imagery data and remote sensing reflectance values, preprocessing and band extraction were performed to accurately extract surface chlorophyll-a concentration information. Next, the underwater chlorophyll-a concentration of the regional water body in vertical sections was determined based on a final underwater concentration model. This final model was derived by correcting the wavelength correlation coefficient based on the initial underwater concentration model.

[0180] In a preferred embodiment of the present invention, the random forest classification model can be trained to realize the detection of three-dimensional chlorophyll a concentration. Match the photon bathymetry data and the multispectral image to the same coordinate system. Since the pixels of the remote sensing image are discrete, and the water depth curve is continuous, the length and position of the water depth curve contained in different pixels are different. Therefore, the water depth value corresponding to each pixel can be calculated by taking the distance weight from the pixel center to the water depth curve segment in the pixel, overcoming the difference between discrete data and continuous data, and realizing effective matching of photon bathymetry data and multispectral image. Construct the coupling relationship between the image pixel spectral information, photon water depth information and chlorophyll a concentration information at the corresponding depth, and establish the pixel spectral sample data and chlorophyll a concentration label data on the depth sequence according to the correlation mechanism and interaction between the information of each element, so as to provide 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 study area is used as the input of the classification model to predict the chlorophyll a concentration values ​​corresponding to the pixels at different water depths, so as to realize the three-dimensional data output of the chlorophyll a concentration area. Combined with Figure 6As shown in the figure, spectral image pixels are first combined with water depth data through coordinate matching to calculate the corresponding water depth. This involves calculating the spectral image pixels, photon water depth curves, and weighted distances. Next, element associations are established, and water depth information is combined with chlorophyll a concentration information using the chlorophyll a concentration variation curve in the depth profile. A classification model is then trained using water depth data, water parameters, spectral imagery, and lidar parameters as input data. This model is then able to predict chlorophyll a concentrations at different underwater depths based on the input data. Finally, the trained classification model is applied to the target study area to generate three-dimensional chlorophyll a concentration detection results, which are presented as 3D graphics showing the three-dimensional distribution of chlorophyll a concentration in the water column.

[0181] In this optional embodiment, by combining the concentration data on the water surface and underwater, a three-dimensional, detailed chlorophyll a concentration distribution map is constructed, breaking through the traditional two-dimensional detection limitations, realizing continuous monitoring from the water surface to underwater, and providing a more accurate and comprehensive data basis.

[0182] Combine Figure 7 As shown, the present invention also provides a chlorophyll a concentration profile three-dimensional detection system, comprising:

[0183] 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 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;

[0184] a refraction correction unit, configured to perform sea 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;

[0185] a nonlinear correction unit, configured to perform nonlinear correction on the photon radiation transmission of the water body in the region by using an underwater profile detection model, and obtain a volume scattering coefficient of the water body in the region at different depths;

[0186] A first model building unit is configured to build 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;

[0187] A second model building unit is configured to model the chlorophyll a concentration of the water surface of the water body in the region according to the image band of the satellite image data, so as to obtain a chlorophyll a concentration model of the water surface of the water body in the region;

[0188] a model correction unit, configured to 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;

[0189] The concentration output unit is used to obtain the three-dimensional chlorophyll a concentration of the water body in the area according to the water surface chlorophyll a concentration model and the final underwater concentration model.

[0190] The present invention's three-dimensional chlorophyll a concentration profile detection system uses a segmented statistical model to divide photon data into surface photons and underwater photons. This detailed segmentation allows accurate identification and extraction of information from different parts of the water body. Because surface photons may be affected by factors such as direct sunlight and waves, while underwater photons provide actual information about the water's interior, segmenting photons helps reduce interference from surface phenomena on underwater data, thereby improving detection accuracy. Next, the original coordinates of the surface photons are reconstructed using wave fitting, and a water photon refraction correction model is constructed to correct the original coordinates of the underwater photons. This corrects for positional errors caused by water refraction, improves the accuracy of the underwater photon coordinates, and ensures detection accuracy in the depth direction. The underwater profile detection model then performs nonlinear correction on photon radiation transmission, recovering lost photon signals and making the detection data closer to the actual situation. Furthermore, a surface chlorophyll a concentration model is established in conjunction with satellite imagery data, enabling the detection method to be expanded from a two-dimensional surface to a three-dimensional space, providing a wide range of surface data through satellite imagery. Finally, the initial underwater concentration model is calibrated and used as the final underwater concentration model, further optimizing depth-direction detection accuracy. By combining surface and underwater concentration models, the method accurately provides a three-dimensional chlorophyll-a concentration distribution for a given area. This not only improves detection accuracy and reliability but also more comprehensively reflects the vertical and horizontal distribution of chlorophyll-a in the water, providing a more accurate and comprehensive data foundation for environmental monitoring and scientific research.

[0191] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A three-dimensional detection method for chlorophyll a concentration profile, characterized in that: include: Acquire satellite image data and water body photon data of a regional water body, wherein the water body photon data includes 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; Performing wave fitting reconstruction based on the original coordinates of the water surface photons to construct 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; Using an underwater profile detection model, nonlinear correction is performed on the photon radiation transmission of the water body in the region to obtain the volume scattering coefficient of the water body in the region at different depths; specifically, the correction includes: By using the underwater profile detection model, the underwater photons are iteratively corrected to obtain a sequence of actual photon quantities at different depths in the water body of the region; Determine the depth of the underwater photons according to the original coordinates of the underwater photons, and determine the expected number sequence of photons at different depths of the regional water body according to the depth in combination with preset radar parameters and water body parameters; Determining the volume scattering coefficients of the regional water body at different depths according to the actual photon number sequence and the expected photon number sequence of the regional water body at different depths; Establishing an initial underwater concentration model of the water body in the region according to the refraction-corrected coordinates of the underwater photons and the volume scattering coefficient of the water body in the region; Modeling the chlorophyll a concentration of the surface of the water body in the region according to the image bands of the satellite image data to obtain a chlorophyll a concentration model of the surface of the water body in the region; The initial underwater concentration model is calibrated according to the image band of the satellite image data, and the calibrated initial underwater concentration model is used as the final underwater concentration model of the regional water body; specifically comprising: determining the water surface chlorophyll a concentration according to the water surface chlorophyll a concentration model; determining a water surface particle scattering coefficient according to an image band of the satellite image data; Inputting the water surface chlorophyll a concentration and the water surface particle scattering coefficient into the initial underwater concentration model, and correcting the wavelength correlation coefficient to obtain a corrected wavelength correlation coefficient; calibrating the initial underwater concentration model according to the correction wavelength correlation coefficient, and using the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; The three-dimensional chlorophyll a concentration of the water body in the region is obtained according to the water surface chlorophyll a concentration model and the final underwater concentration model.

2. The three-dimensional detection method of chlorophyll a concentration profile according to claim 1, characterized in that: The step of 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 into a two-dimensional space along the satellite orbit distance to obtain an elevation-orbit distance distribution map of the photons; Slicing the elevation-track distance distribution graph along the elevation direction to obtain an elevation density histogram of the photons; Obtaining the center elevation of the water surface of the water body in the region according to the elevation density histogram; The photons are divided into the water surface photons and the underwater photons according to the water surface center elevation.

3. The three-dimensional detection method of chlorophyll a concentration profile according to claim 1, characterized in that: The ocean wave fitting reconstruction based on the original coordinates of the water surface photons to construct a water body photon refraction correction model includes: determining the amplitude, angular frequency, direction angle, and wave phase of ocean waves based on 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 wave; The ocean wave model is used to trace the original spatial transmission path of each photon in the water body in the region in combination with the original coordinates of the water surface photons to construct a water body photon refraction correction model.

4. The three-dimensional detection method of chlorophyll a concentration profile according to claim 3, characterized in that: The method of correcting the original coordinates of the underwater photons according to the water body photon refraction model to obtain the refraction-corrected coordinates of the underwater photons includes: Determining, based on the water body photon refraction model, the spatial geometric relationships between the water surface slope angle, the incident angle, and the refraction angle of the water body in the region and the original spatial transmission path; Determining the elevation displacement and the distance displacement along the track of each underwater photon according to the spatial geometric relationship; The original coordinates of the underwater photons are corrected according to the elevation displacement and the distance displacement along the track to obtain the refraction-corrected coordinates.

5. The three-dimensional detection method of chlorophyll a concentration profile according to claim 1, characterized in that: The step of establishing an 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 comprises: determining a correction depth of the underwater photon according to the refraction correction coordinates of the underwater photon; Determining, based on the correction depth, a mathematical model between the correction depth, a reflection angle corresponding to the correction depth, and the volume scattering coefficient; The initial underwater concentration model is established based on the mathematical model combined with the wavelength correlation coefficient of the laser wavelength.

6. The three-dimensional detection method of chlorophyll a concentration profile according to claim 5, characterized in that: The step of modeling the chlorophyll a concentration of the water surface of the regional water body according to the image bands of the satellite image data to obtain the chlorophyll a concentration model of the water surface of the regional water body includes: Preprocessing the satellite image data, and performing band extraction on the preprocessed satellite image data to obtain band images corresponding to the blue band and the green band; The water surface chlorophyll a concentration model is determined based on the band images corresponding to the blue band and the green band and combined with the remote sensing reflectance value of the satellite image data.

7. The three-dimensional detection method of chlorophyll a concentration profile according to claim 1, characterized in that: Obtaining the three-dimensional chlorophyll a concentration of the regional water body according to the water surface chlorophyll a concentration model and the final underwater concentration model includes: determining the surface chlorophyll a concentration of the water body in the region according to the water surface chlorophyll a concentration model; Determining the underwater chlorophyll a concentration of the regional water body in a vertical section according to the final underwater concentration model; The water surface chlorophyll a concentration and the underwater chlorophyll a concentration are used as the three-dimensional chlorophyll a concentration of the water body in the region.

8. A three-dimensional detection system for chlorophyll a concentration profile, characterized in that: include: 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 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 sea 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; The nonlinear correction unit is used to perform nonlinear 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; specifically comprising: By using the underwater profile detection model, the underwater photons are iteratively corrected to obtain a sequence of actual photon quantities at different depths in the water body of the region; Determine the depth of the underwater photons according to the original coordinates of the underwater photons, and determine the expected number sequence of photons at different depths of the regional water body according to the depth in combination with preset radar parameters and water body parameters; Determining the volume scattering coefficients of the regional water body at different depths according to the actual photon number sequence and the expected photon number sequence of the regional water body at different depths; A first model building unit is configured to build 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 building unit is configured to model the chlorophyll a concentration of the water surface of the water body in the region according to the image band of the satellite image data, so as to obtain a chlorophyll a concentration model of the water surface of the water body in the region; A model correction unit is 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; specifically, determining the water surface chlorophyll a concentration according to the water surface chlorophyll a concentration model; determining a water surface particle scattering coefficient according to an image band of the satellite image data; Inputting the water surface chlorophyll a concentration and the water surface particle scattering coefficient into the initial underwater concentration model, and correcting the wavelength correlation coefficient to obtain a corrected wavelength correlation coefficient; calibrating the initial underwater concentration model according to the correction wavelength correlation coefficient, and using the calibrated initial underwater concentration model as the final underwater concentration model of the regional water body; The concentration output unit is used to obtain the three-dimensional chlorophyll a concentration of the water body in the area according to the water surface chlorophyll a concentration model and the final underwater concentration model.

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