Water body environment parameter three-dimensional reconstruction method based on active and passive optical remote sensing

Abstract

The invention discloses a water body environment parameter three-dimensional reconstruction method based on active and passive optical remote sensing, and relates to the technical field of sea area water environment parameter monitoring, and the method comprises the steps: obtaining water body surface water environment parameter data and point cloud data at a water environment parameter vertical depth Dz; performing data matching on the two groups of data to obtain a pixel point closest to the horizontal distance of each point cloud data in the water environment parameter data of the surface layer of the water body, and performing assignment; obtaining a water environment parameter horizontal space distribution data set at the underwater Dz depth through the reconstruction model; and taking the horizontal spatial distribution data of the water environment parameters at the underwater Dz depth as surface data, and obtaining the horizontal spatial distribution data of the water environment parameters at the Dz + x position through a reconstruction model. The method provided by the invention gets rid of the limitation of obtaining the three-dimensional observation data of the water body by using field experimental data in the past, and the obtained result can provide massive three-dimensional omnibearing data support for researchers for researching marine geophysical characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of marine water environment parameter monitoring, in particular to a three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing. Background Art

[0002] Marine water environmental parameters, such as chlorophyll a concentration, suspended matter (SPM), and particulate organic carbon (POC), are important indicators of the state of the marine ecosystem. Traditionally, obtaining vertical data on these parameters relies primarily on on-site sampling and measurement using shipborne instruments. However, this method is costly, inefficient, and has a limited observation range. Existing observation platforms and technologies are unable to conduct large-scale, continuous, three-dimensional assessments of these marine water environmental parameters.

[0003] The development of optical remote sensing satellites and lidar satellite technology has provided new means for monitoring the marine environment. Optical remote sensing satellites can obtain sea surface reflectivity information, which can be used to infer surface water environmental parameters; lidar satellites can obtain vertical profile information of water bodies, which can be used to detect internal water structure. However, optical remote sensing satellite data only captures the spatial distribution of surface ecological and environmental parameters, while lidar satellites obtain vertical profile point cloud data. Currently, there is a lack of algorithmic models that can effectively integrate these two types of satellite data to invert the vertical three-dimensional spatial distribution of water environmental ecological parameters over large marine areas. Summary of the Invention

[0004] In order to overcome the above problems existing in the prior art, the present invention proposes a three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, comprising the following steps: Step 1: Determine the research area and time period, obtain the surface water environment parameter data C_level1 and the point cloud data Chlapro at the vertical depth D_z of the water environment parameter, and record the total number of point cloud data Chlapro as num1; Step 2: Match the two sets of data obtained in step 1 to obtain the pixel point with the closest horizontal distance to each point cloud data in the surface water environment parameter data of the water body, and assign a value; Step 3: Reconstruct the model of the horizontal spatial distribution data of water environment parameters at the vertical depth D_z of the water body to obtain the horizontal spatial distribution data set C_level2 of the water environment parameters at the depth D_z underwater; Step 4: Using the horizontal spatial distribution data of the water environment parameters at the underwater depth D_z as the surface data, the horizontal spatial distribution data of the water environment parameters at D_z+x is obtained through the reconstruction model described in step 3.

[0006] The above-mentioned three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, said step 1 specifically includes: obtaining water surface water environment parameter data C_level1 through water remote sensing reflectance spectrum data based on optical remote sensing data, and obtaining water environment parameter point cloud data Chlapro at vertical depth D_z based on lidar remote sensing data or on-site measurement; The laser radar remote sensing data is calculated through the laser attenuation coefficient of the ocean water body to obtain the point cloud data Chlapro at the vertical depth D_z of the water environment parameters.

[0007] The above-mentioned three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, the assignment process in step 2 is specifically as follows: the water environment parameter data matrix at the underwater depth D_z that corresponds one-to-one to the surface water environment parameter data of the water body is S_level2, the initial value is NaN, and the pixel point in the surface water environment parameter data that is closest to each point cloud data in the horizontal distance is assigned at the corresponding position of S_level2 so that it is equal to the point cloud data at the vertical depth D_z of the water environment parameter.

[0008] In the above-mentioned method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing, step 3 specifically comprises: calculating the proportion of the data volume matched in step 2 in the total number of pixels; if the proportion is greater than k, initializing C_level2 so that C_level2 is equal to C_level1; injecting the known data points in S_level2 into C_level2; constructing the objective function T based on the correlation between C_level1 and C_level2 and the smoothness of C_level2 itself; setting constraints and gradient functions; and finding the minimum value of the objective function through an optimization algorithm; If the proportion is not greater than k, take each obtained pixel point as the center point and expand it to a×a pixels in all directions. Intercept the data set of C_level1 in the a×a area and mark it as S_level1. At the same time, take the S_level2 data at the pixel point and mark it as T_level2. Normalize the S_level1 data to obtain a×a weight matrix. Apply the weight matrix to reconstruct the water environment parameters of the lower layer S_level2 matrix at the corresponding a×a position to obtain S_level2'. Reconstruct S_level1 according to the above method for all matching points, and calculate whether the proportion of the reconstructed S_level2 valid data in the total pixel volume exceeds k. If the proportion is still not greater than k, then expand it to a×a pixels in all directions with the valid data edge point of S_level2' as the center, and continue to reconstruct until the proportion of S_level2 valid data in the total pixel volume exceeds k. At this time, the effective data amount of S_level2' is reassigned to num1, and the relevant operations with a proportion greater than k are performed.

[0009] The objective function of the above-mentioned three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing is expressed as: ; ; ; ; Where a1 is the consistency weight with the surface layer, a2 is the row smoothing weight, and a3 is the column smoothing weight; T1 is the sum of squares of the difference between the underlying data and the surface data multiplied by the weight, T2 is the sum of squares of the first-order differences in the row direction of the underlying data multiplied by the weight, and T3 is the sum of squares of the first-order differences in the column direction of the underlying data multiplied by the weight; m and n are the number of rows and columns in the arrays C_level1 and C_level2; i and j are array subscripts, with i ranging from [1 to m] and j ranging from [1 to n].

[0010] In the above-mentioned three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, the gradient function is expressed as: ; ; ; ; Among them, grad is the gradient function.

[0011] The present invention has the beneficial effect of utilizing active and passive remote sensing data to determine the horizontal spatial distribution of water environmental parameters (such as chlorophyll a concentration, suspended matter concentration, and particulate organic carbon concentration) over a wide area of ​​the ocean at any depth (within the effective detection depth of a laser radar). This method breaks away from the previous limitation of using field experimental data to obtain three-dimensional observation data of water bodies. Instead, it proposes a reconstruction model that can invert the three-dimensional values ​​of water environmental parameters based on optical remote sensing data and laser radar detection data. The obtained results can provide researchers studying marine geophysical properties with massive, comprehensive three-dimensional data support, solving the problem of inadequate research on marine geophysical properties due to the lack of vertical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 3 is a schematic diagram of a processing process when the amount of matched data accounts for no more than 30% of the total amount of pixels in an embodiment of the present invention; Figure 32 is a schematic diagram of a processing process when the amount of matched data accounts for more than 30% of the total pixel amount in an embodiment of the present invention; Figure 4 Schematic diagram of the reconstruction results in the embodiment of the present invention, where (a) is the surface distribution result of chlorophyll a concentration in the target sea area in November 2013; (b) is the surface distribution result of chlorophyll a concentration point cloud data in the target sea area in November 2013. Figure 2 (c) is the distribution result of chlorophyll a concentration level at a depth of 5m underwater in the target sea area in November 2013 reconstructed by this algorithm. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] The present invention discloses a three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, comprising the following steps: Step 1: Determine the study area and time period, obtain the surface water environment parameter data C_level1 and the point cloud data Chlapro at the vertical depth D_z of the water environment parameter, and record the total number of point cloud data Chlapro as num1.

[0015] Step 1 specifically includes: the surface water environment parameter data C_level1 is obtained through water body remote sensing reflectance spectrum data based on optical remote sensing data, and the point cloud data Chlapro at the vertical depth D_z of the water environment parameter is obtained by lidar remote sensing data or on-site measurement; The laser radar remote sensing data is calculated through the laser attenuation coefficient of the ocean water body to obtain the point cloud data Chlapro at the vertical depth D_z of the water environment parameters.

[0016] Step 2: Match the two sets of data obtained in step 1 to obtain the pixel point with the closest horizontal distance to each point cloud data in the surface water environment parameter data of the water body, and assign a value.

[0017] The specific assignment process in step 2 is as follows: the water environment parameter data matrix at the underwater depth D_z that corresponds one-to-one to the surface water environment parameter data of the water body is S_level2, and the initial value is NaN. The pixel point in the surface water environment parameter data that is closest to each point cloud data in the horizontal distance is assigned at the corresponding position of S_level2 so that it is equal to the point cloud data at the vertical depth D_z of the water environment parameter.

[0018] Step 3: Reconstruct the model of the horizontal spatial distribution data of water environment parameters at the vertical depth D_z of the water body to obtain the horizontal spatial distribution dataset C_level2 of the water environment parameters at the depth D_z underwater.

[0019] Step 3 is as follows: Calculate the proportion of the data matched in step 2 to the total number of pixels. If the proportion is greater than k, initialize C_level2 so that C_level2 is equal to C_level1. At the same time, inject the known data points in S_level2 into C_level2. Based on the correlation between C_level1 and C_level2 and the smoothness of C_level2, construct the objective function T. Set the constraints and gradient function, and find the minimum value of the objective function through the optimization algorithm. If the proportion is not greater than k, take each obtained pixel point as the center point and expand it to a×a pixels in all directions. Intercept the data set of C_level1 in the a×a area and mark it as S_level1. At the same time, take the S_level2 data at the pixel point and mark it as T_level2. Normalize the S_level1 data to obtain a×a weight matrix. Apply the weight matrix to reconstruct the water environment parameters of the lower layer S_level2 matrix at the corresponding a×a position to obtain S_level2'. Reconstruct S_level1 according to the above method for all matching points, and calculate whether the proportion of the reconstructed S_level2 valid data in the total pixel volume exceeds k. If the proportion is still not greater than k, then expand it to a×a pixels in all directions with the valid data edge point of S_level2' as the center, and continue to reconstruct until the proportion of S_level2 valid data in the total pixel volume exceeds k. At this time, the effective data amount of S_level2' is reassigned to num1, and the relevant operations with a proportion greater than k are performed.

[0020] The objective function is expressed as: ; ; ; ; Where a1 is the consistency weight with the surface layer, a2 is the row smoothing weight, and a3 is the column smoothing weight; T1 is the sum of squares of the difference between the underlying data and the surface data multiplied by the weight, T2 is the sum of squares of the first-order differences in the row direction of the underlying data multiplied by the weight, and T3 is the sum of squares of the first-order differences in the column direction of the underlying data multiplied by the weight; m and n are the number of rows and columns in the arrays C_level1 and C_level2, and i and j are the array subscripts, with i ranging from [1 to m] and j ranging from [1 to n].

[0021] The gradient function is expressed as: ; ; ; ; Among them, grad is the gradient function.

[0022] Step 4: Using the horizontal spatial distribution data of the water environment parameters at the underwater depth D_z as the surface data, the horizontal spatial distribution data of the water environment parameters at D_z+x is obtained through the reconstruction model described in step 3.

[0023] In this embodiment, the reconstruction of the chlorophyll a concentration level distribution data set at the depth D_z in water is used as an example to illustrate the specific implementation of the method of the present invention. The chlorophyll a concentration here can be replaced by any water environment parameter data such as suspended matter concentration SPM, particulate organic carbon concentration POC, etc. The specific steps are as follows: Figure 1 shown.

[0024] Determine the study area and time period, and search for optical remote sensing data and lidar remote sensing data within the study area. The chlorophyll a concentration data C_level1 (assuming m rows and n columns) of the surface layer of the water body can be obtained from the optical remote sensing data through the water body remote sensing reflectance spectrum Rrs(λ) data obtained from it. The chlorophyll a concentration of each pixel is recorded as C_level1(log_i,lat_j,0). Known inversion algorithms include YOC, OC2, OC3 and neural network methods. The appropriate inversion method can be selected according to the different sea areas studied. The ocean water lidar attenuation coefficient can also be obtained from the lidar remote sensing data. K lidar (λ, z) is calculated to obtain the chlorophyll a concentration profile point cloud data of the water body, where the chlorophyll a concentration at the depth D_z is recorded as Chla pro (log_0,lat_0,D_z), the total number of chlorophyll a concentration data at the depth of D_z is recorded as num1 (num1≥1). It is worth noting that Chla pro (log_0,lat_0,D_z) can be obtained not only from lidar remote sensing data, but also from measured data obtained through field experiments.

[0025] The surface chlorophyll a concentration data C_level1(log_i,lat_j,0) is compared with the num1 chlorophyll a concentration Chla at the depth of D_z proData matching is performed on the (log_0, lat_0, D_z) data. The principle of spatiotemporal matching can be determined according to needs. The num1 pixel points (log_x, lat_y) in the surface chlorophyll a concentration data that are horizontally closest to each point cloud data position (log_0, lat_0) are obtained. Assuming that the chlorophyll a concentration data matrix at the depth D_z that corresponds one-to-one with the surface data is S_level2 (m rows, n columns), and the initial values ​​are all NaN, the num1 pixel points (log_x, lat_y) are assigned values ​​at the corresponding positions in S_level2 to make them equal to the chlorophyll a concentration point cloud data at the depth D_z, as shown in the following formula: S_level2(log_x,lat_y,D_z)=Chla pro (log_0,lat_0,D_z).

[0026] Based on this, the known items are C_level1 and S_level2. S_level2 contains num1 matched data point values ​​S_level2 known , unknown data (i.e. data that needs to be completed, S_level2 missing ) is represented by NaN. In order to complete the unknown data in the S_level2 data to obtain a complete chlorophyll a concentration spatial distribution dataset C_level2 at the depth D_z (i.e., the completed S_level2), this embodiment constructs a model for solving an optimization problem.

[0027] Refactoring the model First, determine whether the matching data volume num1 accounts for 30% of the total pixel volume m×n (this indicator is obtained from multiple experiments).

[0028] If num1 / (m×n)≤30%, such as Figure 2 As shown, each pixel point (log_x, lat_y) is taken as the center point and expanded to 3×3 pixels in all directions. The data set of the surface chlorophyll a concentration C_level1 in the 3×3 area is intercepted and labeled as S_level1. At the same time, the S_level2 data at the pixel point is taken and labeled as T_level2. The S_level1 data is normalized to obtain a 3×3 weight matrix: Weight=S_level1(i,j) / sum(S_level1); Apply this weight matrix to reconstruct the chlorophyll a concentration at the corresponding 3×3 position of the lower layer S_level2 matrix to obtain the new S_level2: Tol=3×3×T_level2; S_level2(log_x-1:log_x+1,lat_y-1:lat_y+1,D_z)=Weight×Tol; Reconstruct the S_level2 data using the above method for all num1 matching points. Calculate whether the reconstructed S_level2 valid data accounts for 30% of the total m×n data. If it is still less than 30%, expand the area around the edge of the S_level2 valid data to 3×3 pixels. Continue the above algorithm to reconstruct the data until the S_level2 valid data accounts for more than 30% of the total m×n data. At this time, reassign the S_level2 valid data amount to num1, and then proceed to the next step.

[0029] If num1 / (m×n)>30%, such as Figure 3 As shown, first assign an initial value to C_level2 to make it equal to the surface dataset C_level1, and at the same time assign the value of num1 known data points in S_level2 to S_level2. known Injected into C_level2: ; .

[0030] Then, based on the correlation between the surface data C_level1 and the lower data C_level2, as well as the smoothness of the lower data C_level2 itself, we construct the objective function T. T consists of three parts: the sum of squares of the difference between the lower data and the surface data multiplied by the weight (T1), the sum of squares of the first-order differences in the row direction of the lower data multiplied by the weight (T2), and the sum of squares of the first-order differences in the column direction of the lower data multiplied by the weight (T3), as shown in the following formula: ; ; ; ; Where a1 is the consistency weight with the surface, a2 is the row smoothing weight, and a3 is the column smoothing weight, with values ​​ranging from 0 to 1.

[0031] The gradients of these three parts are: ; ; ; The total gradient grad is the sum of the three gradients: .

[0032] Next, set the constraints: Known underlying data sets are fixed values, and the values ​​of these points remain unchanged during the optimization process. That is: ; Set the maximum number of iterations max_iter and the convergence tolerance Ctol.

[0033] fmincon is a function used to solve constrained nonlinear optimization problems. It allows users to provide an objective function, a gradient function, and various constraints, and then uses the optimization algorithm to find the minimum of the objective function. The algorithm model of the present invention finally uses MATLAB's fmincon function to solve the optimization problem using the interior-point method based on the objective function T and the gradient function grad, as well as the set constraints, to obtain the reconstructed lower-level chlorophyll a concentration data C_level2.

[0034] It is particularly noteworthy that when reconstructing the chlorophyll a concentration level spatial distribution data set at D_z+x, which is deeper than the depth of D_z, the chlorophyll a concentration data at the depth of D_z is used as the surface data, and the chlorophyll a concentration data at D_z+x is used as the lower data to repeat the above reconstruction model. This processing can obtain the chlorophyll a concentration level spatial distribution data results at D_z+x with higher reconstruction accuracy.

[0035] Using the above algorithm model, we numerically reconstructed the spatial distribution dataset C_level2 of the monthly average chlorophyll-a concentration at a depth of 5m in the sea area between 34-36°N and 121-123°E in November 2013. Given the monthly average chlorophyll-a concentration C_level1 of the surface water in this sea area in November 2013 obtained from MODIS satellite data, and the chlorophyll-a concentration values ​​S_level2 of some points at a depth of 5m obtained from field experimental data in November 2013, the reconstruction algorithm yielded the monthly average chlorophyll-a concentration distribution at a depth of 5m in November 2013. The results are shown in the figure below. Figure 4 shown. Figure 4 (a) is the surface distribution of chlorophyll a concentration in the target sea area in November 2013; Figure 4 (b) is the target sea area in November 2013 by the on-site chlorophyll a concentration point cloud data Figure 2 The distribution results obtained by the steps shown; Figure 4 (c) is the horizontal distribution result of chlorophyll a concentration at a depth of 5m in the target sea area in November 2013 reconstructed by this algorithm. It can be found that the horizontal distribution result of underwater chlorophyll a concentration reconstructed by this algorithm ( Figure 4(c) The coverage area is wide and the data is continuous.

[0036] The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the spirit and scope of protection of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present invention.

Claims

1. A three-dimensional reconstruction method of water environment parameters based on active and passive optical remote sensing, characterized in that: The steps include: Step 1, determine the research area and time period, obtain the surface water environment parameter data C_level1, the point cloud data Chlapro at the vertical depth D_z of the water environment parameter, and record the total number of point cloud data Chlapro as num1; Step 2, matching the two sets of data obtained in step 1, obtaining the pixel point in the surface water environment parameter data that is closest to each point cloud data horizontally, and assigning a value; Step 3, reconstructing the model of the horizontal spatial distribution data of water environment parameters at the vertical depth D_z of the water body to obtain the horizontal spatial distribution data set C_level2 of the water environment parameters at the underwater depth D_z; Step 4, using the horizontal spatial distribution data of water environment parameters at the underwater depth D_z as surface data, and obtaining the horizontal spatial distribution data of water environment parameters at D_z+x through the reconstruction model described in step 3.

2. The method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing according to claim 1, characterized in that: The step 1 specifically includes: the water surface water environment parameter data C_level1 is obtained through water body remote sensing reflectance spectrum data according to optical remote sensing data, and the point cloud data Chlapro at the vertical depth D_z of the water environment parameter is obtained by laser radar remote sensing data or on-site measurement; The laser radar remote sensing data is calculated through the laser attenuation coefficient of the ocean water body to obtain the point cloud data Chlapro at the vertical depth D_z of the water environment parameters.

3. The method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing according to claim 1, characterized in that: The assignment process in step 2 is specifically as follows: the water environment parameter data matrix at the underwater depth D_z that corresponds one-to-one to the water body surface water environment parameter data is S_level2, the initial value is NaN, and the pixel point in the water body surface water environment parameter data that is closest to each point cloud data in horizontal distance is assigned at the corresponding position of S_level2 to make it equal to the point cloud data at the vertical depth D_z of the water environment parameter.

4. The method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing according to claim 3, characterized in that: The step 3 is specifically as follows: calculating the proportion of the amount of data matched in step 2 in the total amount of pixels, if the proportion is greater than k, assigning an initial value to C_level2 so that C_level2 is equal to C_level1, and injecting the known data points in S_level2 into C_level2, constructing the objective function T based on the correlation between C_level1 and C_level2 and the smoothness of C_level2 itself, setting constraints and gradient functions, and finding the minimum value of the objective function through the optimization algorithm; If the proportion is not greater than k, take each pixel point obtained as the center point and expand to a×a pixels in all directions, intercept the data set of C_level1 in the a×a area, mark it as S_level1, and take the S_level2 data at the pixel point as T_level2, normalize the S_level1 data to obtain a×a weight matrix, apply the weight matrix to reconstruct the water environment parameters of the lower S_level2 matrix at the corresponding a×a position, and obtain S_level2'; reconstruct S_level1 for all matching points according to the above method, calculate whether the proportion of reconstructed S_level2 valid data in the total pixel volume exceeds k, if the proportion is still not greater than k, then expand to a×a pixels in all directions with the valid data edge point of S_level2' as the center, and continue to reconstruct until the proportion of S_level2 valid data in the total pixel volume exceeds k, at this time, reassign the amount of S_level2' valid data to num1, and perform related operations with a proportion greater than k.

5. The method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing according to claim 4, characterized in that: The objective function is expressed as: ; ; ; ; Where a1 is the consistency weight with the surface layer, a2 is the row smoothing weight, and a3 is the column smoothing weight; T1 is the sum of squares of the difference between the underlying data and the surface data multiplied by the weight, T2 is the sum of squares of the first-order differences in the row direction of the underlying data multiplied by the weight, and T3 is the sum of squares of the first-order differences in the column direction of the underlying data multiplied by the weight; m and n are the number of rows and columns in the arrays C_level1 and C_level2; i and j are array subscripts, i is between [1 m] and j is between [1 n].

6. The method for three-dimensional reconstruction of water environment parameters based on active and passive optical remote sensing according to claim 4, characterized in that: The gradient function is expressed as: ; ; ; ; Among them, grad is the gradient function.

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