Oceanic-waters subsurface chlorophyll-a maxima depth retrieval method based on remote sensing reflectance

Abstract

The present invention relates to the technical field of ocean color remote sensing. Disclosed is an oceanic-waters subsurface chlorophyll-a maxima depth retrieval method based on remote sensing reflectance. The method comprises the following step: S1: selecting wave bands, acquiring a remote sensing reflectance product among MODIS remote sensing products, selecting remote sensing reflectance data at three wave bands, i.e., 412 nm, 555 nm, and 678 nm, and performing preprocessing. The present invention can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentration, sea surface temperature data and sea surface height data, reducing the consumption of computational resources for running machine learning, but can also effectively improve the retrieval accuracy, and can also reduce the consumption of computational resources for the retrieval of subsurface chlorophyll-a maxima and reduce the retrieval difficulty.

Description

A method for retrieving the maximum depth of chlorophyll-a concentration in the subsurface of ocean waters based on remote sensing reflectivity Technical Field

[0001] The present invention relates to the technical field of water color remote sensing, in particular to a method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water bodies based on remote sensing reflectivity. Background Art

[0002] Phytoplankton are a vital component of the ocean and play a crucial role in marine ecosystems, contributing approximately 50% of global primary productivity. Chlorophyll a (Chl-a), the primary pigment within marine phytoplankton cells, is a key indicator of marine phytoplankton abundance. As producers in marine ecosystems, marine phytoplankton provide essential materials and energy through photosynthesis. They also serve as a natural food source for shellfish, shrimp, and fish larvae, and these organisms migrate in response to changes in chlorophyll a concentration. However, the vertical distribution of chlorophyll a concentration in the ocean is uneven. Traditional remote sensing measurements of chlorophyll a concentration often integrate the chlorophyll a concentration across the entire water column. Therefore, observing the vertical structure of chlorophyll a concentration has become a pressing challenge in ocean color remote sensing. Research has shown that chlorophyll a concentration often reaches a maximum in the subsurface layer of the ocean, known as the subsurface chlorophyll a maximum (SCM). The depth of maximum chlorophyll-a concentration in the ocean's subsurface is an important indicator for estimating its vertical distribution. It is also a key parameter for estimating global carbon flux and primary productivity, a crucial indicator for measuring the ecological effects of global climate change, and a key parameter urgently needed for remote sensing of ocean color. Remote sensing reflectance signals in oceanic regions are primarily influenced by the vertical distribution and concentration of chlorophyll-a. The remote sensing reflectance signature generated by this distribution effectively indicates the depth of maximum subsurface chlorophyll-a concentration. Determining the depth of maximum subsurface chlorophyll-a concentration based on remote sensing reflectance inversion is a crucial foundation for understanding the state of ocean primary productivity. This has significant implications for macroscale aquatic ecological protection, the global carbon cycle, and global climate change.

[0003] The inversion of subsurface chlorophyll a concentration in the ocean currently mainly includes the subsurface chlorophyll a concentration maximum depth inversion based on the Gaussian kernel function model and the subsurface chlorophyll a concentration maximum depth inversion based on machine learning. The details are as follows: (1) Subsurface chlorophyll a concentration maximum depth inversion method based on the Gaussian kernel function model: The subsurface chlorophyll a concentration maximum depth inversion method based on the Gaussian kernel function first assumes that the vertical distribution of chlorophyll a concentration conforms to the Gaussian function: where Zscm The depth of the subsurface chlorophyll a maximum is the depth where the subsurface chlorophyll a maximum is located. This method can be used to infer the depth of the subsurface chlorophyll a maximum using the chlorophyll a concentration of the sea surface inverted by satellite. However, this method is very dependent on the inversion accuracy of the surface chlorophyll a concentration, and the chlorophyll a concentration does not all conform to the Gaussian function distribution. Therefore, the inversion accuracy of this method is low, and the calculation method is complex and the amount of calculation is large. (2) Subsurface chlorophyll a maximum depth inversion method based on machine learning: The subsurface chlorophyll a maximum depth inversion method based on machine learning currently requires the input of many parameters, such as sea surface temperature, sea surface height and sea surface chlorophyll a concentration. Many parameters are required. In addition, the subsurface chlorophyll a maximum depth inversion method based on machine learning also requires a large amount of chlorophyll a profile data for training. A large amount of computing resources are consumed in the process of machine learning model training, which is very costly. Therefore, it is particularly important to propose a subsurface chlorophyll a concentration maximum depth algorithm that directly relies on remote sensing reflectance.

[0004] In addition, the subsurface chlorophyll a maximum depth inversion based on the Gaussian kernel function model and the subsurface chlorophyll a maximum depth inversion based on machine learning still have the disadvantage of lacking an effective optical physics basis, and cannot make good use of the remote sensing reflectance characteristics caused by the depth changes of the subsurface chlorophyll a maximum.

[0005] Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a method for depth inversion of the maximum subsurface chlorophyll a concentration in ocean water based on remote sensing reflectivity, which is mainly used to solve the problem that the existing subsurface chlorophyll a maximum depth inversion based on the Gaussian kernel function model and the subsurface chlorophyll a maximum depth inversion based on machine learning lack an effective optical physics basis.

[0008] (2) Technical solution

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method for inverting the maximum depth of chlorophyll-a concentration in the subsurface layer of ocean water based on remote sensing reflectivity comprises the following steps:

[0011] S1: Select the band to obtain the remote sensing reflectance product from the MODIS remote sensing product, select the remote sensing reflectance data of the three bands of 412nm, 555nm, and 678nm, and perform preprocessing;

[0012] S2: Calculate the R value by calculating the difference between the remote sensing reflectance of the 412nm and 678nm bands, and then calculate the ratio of this difference to the 555nm remote sensing reflectance data to obtain R;

[0013] S3: Collect data, extract the global measured BGC-Argo chlorophyll a concentration profile data, use the Gaussian function to fit the BGC-Argo chlorophyll a profile, and select the depth of the maximum chlorophyll a concentration as the Z SCM , calculate the subsurface chlorophyll a maximum depth of each BGC-Argo data and record the longitude and latitude;

[0014] S4: Build a model, match the longitude and latitude data provided by the BGC-Argo float with the corresponding longitude and latitude MODIS remote sensing reflectance, and average the R values ​​obtained by calculating the remote sensing reflectance data of the two grid points around the grid point obtained by the BGC-Argo longitude and latitude positioning as a set of data. Then, follow this step to pair the global BGC-Argo data, use the averaged R value as the input feature, and the subsurface chlorophyll a maximum depth as the output feature to obtain the matching data set. SCM Draw a scatter plot of the two with the R value to obtain a nonlinear model;

[0015] S5: Calculate Z SCM According to the nonlinear model in S4, the function expression of the nonlinear model is obtained. On the basis of the function expression of the nonlinear model, the remote sensing reflectance characteristic R value of the vertical distribution of different chlorophyll a concentrations is combined to perform regression to obtain the optimal depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water.

[0016] Furthermore, the R value is a ratio of the remote sensing reflectances at 412 nm, 555 nm and 678 nm after a simple calculation.

[0017] Based on the above scheme, the calculation formula of the R value is:

[0018] As a further solution of the present invention, the functional expression of the nonlinear statistical model is: SCM =a0+a1X+a2X 2 .

[0019] Furthermore, the unknown number X is replaced by the remote sensing reflectivity characteristic function R of the 412nm, 555nm, and 678nm bands, and the functional expression of the obtained nonlinear statistical model is finally:

[0020] On the basis of the above scheme, the Z SCMIt is obtained by fitting the global BGC-Argo chlorophyll a concentration profile using a Gaussian kernel function. SCM The specific fitting method is Where Chla0 is the sea surface chlorophyll a concentration, σ is the variance of chlorophyll a concentration (4σ is the Gaussian peak width), h is the total chlorophyll a concentration within the Gaussian peak width, and Z SCM is the depth of maximum concentration in the profile, and Z is the water depth.

[0021] As a further solution of the present invention, a0, a1, and a2 are coefficients obtained by pairing the global BGC-Argo chlorophyll a concentration profile with the remote sensing reflectance spectral characteristic R value and performing nonlinear fitting.

[0022] Furthermore, the Z is obtained by BGC-Argo SCM The corresponding R value data is obtained by matching the R value of the corresponding longitude and latitude and time around the world using the longitude and latitude data of BGC-Argo.

[0023] (3) Beneficial effects

[0024] Compared with the existing technology, the present invention provides a method for inverting the maximum depth of chlorophyll-a concentration in the subsurface layer of ocean water based on remote sensing reflectivity, which has the following beneficial effects:

[0025] 1. The present invention uses a nonlinear regression model combined with the remote sensing reflectance characteristics of 412nm, 555nm and 678nm caused by the vertical distribution of different chlorophyll a concentrations to invert the depth of the maximum chlorophyll a concentration in the subsurface layer of ocean water. It can effectively include the optical characteristic signals caused by the vertical distribution of different chlorophyll a concentrations, and can independently rely on the remote sensing reflectance data obtained by satellite remote sensing to simply and quickly invert the depth of the maximum chlorophyll a concentration in the subsurface layer of ocean water. The calculation amount is small, which reduces the dependence of machine learning on sea surface chlorophyll a concentration data, sea surface temperature data, and sea surface height data, and reduces the computing resource consumption of running machine learning.

[0026] 2. Compared with the subsurface chlorophyll a maximum depth inversion method based on the Gaussian kernel function, the subsurface chlorophyll a maximum depth inversion method of the present invention uses more optical characteristic signals, effectively improving the inversion accuracy.

[0027] 3. Comparing the scatter plot of the subsurface chlorophyll-a maximum depth obtained by the proposed method for inverting the depth of the subsurface chlorophyll-a concentration maximum in ocean waters based on remote sensing reflectivity with the subsurface chlorophyll-a maximum depth obtained from global BGC-Argo data reveals that this method performs well for inverting the global subsurface chlorophyll-a maximum depth, with a MAE of 10.87, a coefficient of determination (R²) of 0.85, and an RMSE of 13.75. The scatter plots are concentrated near the y=x function, resulting in high inversion accuracy. The computational effort is significantly reduced compared to machine learning-based methods for inverting the depth of the subsurface chlorophyll-a maximum, effectively reducing the computational resource consumption and difficulty of inverting the subsurface chlorophyll-a maximum. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] FIG1 is a schematic diagram of the process structure of a method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity proposed by the present invention;

[0029] FIG2 is a graph showing the relationship between the R value used in the present invention and the depth of the maximum value of subsurface chlorophyll a;

[0030] FIG3 is a comparison diagram of the maximum depth of subsurface chlorophyll a inverted by the present invention and the maximum depth of subsurface chlorophyll a observed by BGC-Argo. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] Example 1

[0033] 1 to 3 , a method for inverting the maximum depth of chlorophyll-a concentration in the subsurface layer of ocean water based on remote sensing reflectivity includes the following steps:

[0034] S1: Select bands to obtain remote sensing reflectance products from MODIS remote sensing products. Select remote sensing reflectance data of three bands: 412nm, 555nm, and 678nm, and perform preprocessing.

[0035] S2: Calculate the R value by calculating the difference between the remote sensing reflectance of the 412nm and 678nm bands, and then calculate the ratio of this difference to the 555nm remote sensing reflectance data to obtain R;

[0036] S3: Collect data, extract the global measured BGC-Argo chlorophyll a concentration profile data, use the Gaussian function to fit the BGC-Argo chlorophyll a profile, and select the depth of the maximum chlorophyll a concentration as the Z SCM , calculate the subsurface chlorophyll a maximum depth of each BGC-Argo data and record the longitude and latitude. The extracted BGC-Argo chlorophyll a concentration profile data is located in the equatorial Pacific, northwest Pacific, Southern Ocean, Indian Ocean, Atlantic Ocean and other regions. These regions include not only high chlorophyll a concentration areas, but also low chlorophyll a concentration areas, which has strong universality;

[0037] S4: Build a model, match the longitude and latitude data provided by the BGC-Argo float with the corresponding longitude and latitude MODIS remote sensing reflectance, and average the R values ​​obtained by calculating the remote sensing reflectance data of the two grid points around the grid point obtained by the BGC-Argo longitude and latitude positioning as a set of data. Then, follow this step to pair the global BGC-Argo data, use the averaged R value as the input feature, and the subsurface chlorophyll a maximum depth as the output feature to obtain the matching data set. SCM Draw a scatter plot of the two with the R value to obtain a nonlinear model;

[0038] S5: Calculate Z SCM According to the nonlinear model in S4, the function expression of the nonlinear model is calculated, and the optimal depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water body is obtained by regression based on the function expression of the nonlinear model combined with the remote sensing reflectance characteristic R value of different chlorophyll a concentration vertical distribution. The nonlinear regression model is combined with the remote sensing reflectance characteristics of 412nm, 555nm and 678nm caused by the vertical distribution of different chlorophyll a concentrations to invert the depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water body. It can effectively include the optical characteristic signals caused by the vertical distribution of different chlorophyll a concentrations, and can independently rely on the remote sensing reflectance data obtained by satellite remote sensing to simply and quickly invert the depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water body. The calculation amount is small, which reduces the dependence of machine learning on sea surface chlorophyll a concentration, sea surface temperature data, and sea surface height data, and reduces the computing resource consumption of running machine learning.

[0039] In the present invention, the R value is the ratio of the remote sensing reflectance at 412 nm, 555 nm and 678 nm after simple calculation. The calculation formula of the R value is: The functional expression of the nonlinear statistical model is: SCM =a0+a1X+a2f 2, replacing the unknown number X with the remote sensing reflectivity characteristic function R of the 412nm, 555nm, and 678nm bands, the functional expression of the obtained nonlinear statistical model is finally: The Z SCM It is obtained by fitting the global BGC-Argo chlorophyll a concentration profile using a Gaussian kernel function. SCM The specific fitting method is Where Chla0 is the sea surface chlorophyll a concentration, σ is the variance of chlorophyll a concentration (4σ is the Gaussian peak width), h is the total chlorophyll a concentration within the Gaussian peak width, and Z SCM is the depth of the maximum profile concentration, Z is the water depth, a0, a1, a2 are the coefficients obtained by pairing the global BGC-Argo chlorophyll a concentration profile with the remote sensing reflectance spectral characteristic R value and performing nonlinear fitting, and Z is obtained by BGC-Argo. SCM The corresponding R value data is obtained by matching the global R value for the corresponding longitude and latitude and time using the BGC-Argo longitude and latitude data. A scatter plot comparing the subsurface chlorophyll-a maximum depth obtained by the proposed method for inverting the depth of subsurface chlorophyll-a concentration in ocean waters based on remote sensing reflectivity with the subsurface chlorophyll-a maximum depth obtained from global BGC-Argo data shows that this method is effective in inverting the global subsurface chlorophyll-a maximum depth, with a MAE of 10.87, a coefficient of determination (R2) of 0.85, and an RMSE of 13.75. The scatter plot distribution is concentrated near the y=x function, indicating high inversion accuracy. The calculation time on a typical desktop computer platform is only 10 seconds, significantly reducing the computational effort compared to the machine learning-based subsurface chlorophyll-a maximum depth inversion method, effectively reducing the computational resource consumption and difficulty of inverting the subsurface chlorophyll-a maximum.

[0040] Example 2

[0041] 1 to 3 , a method for inverting the maximum depth of chlorophyll-a concentration in the subsurface layer of ocean water based on remote sensing reflectivity includes the following steps:

[0042] S1: Select the band to obtain the remote sensing reflectance product from the MODIS remote sensing product, select the remote sensing reflectance data of the three bands of 412nm, 555nm, and 678nm, and perform preprocessing;

[0043] S2: Calculate the R value by calculating the difference between the remote sensing reflectance of the 412nm and 678nm bands, and then calculate the ratio of this difference to the 555nm remote sensing reflectance data to obtain R;

[0044] S3: Collect data, extract the global measured BGC-Argo chlorophyll a concentration profile data, use the Gaussian function to fit the BGC-Argo chlorophyll a profile, and select the depth of the maximum chlorophyll a concentration as the Z SCM , calculate the subsurface chlorophyll a maximum depth of each BGC-Argo data and record the longitude and latitude. The BGC-Argo chlorophyll a concentration profile data extracted by B is located in the equatorial Pacific, northwest Pacific, Southern Ocean, Indian Ocean, Atlantic Ocean and other regions. These regions include not only high chlorophyll a concentration areas, but also low chlorophyll a concentration areas, which has strong universality;

[0045] S4: Build a model, match the longitude and latitude data provided by the BGC-Argo float with the corresponding longitude and latitude MODIS remote sensing reflectance, and average the R values ​​obtained by calculating the remote sensing reflectance data of the two grid points around the grid point obtained by the BGC-Argo longitude and latitude positioning as a set of data. Then, follow this step to pair the global BGC-Argo data, use the averaged R value as the input feature, and the subsurface chlorophyll a maximum depth as the output feature to obtain the matching data set. SCM Draw a scatter plot of the two with the R value to obtain a nonlinear model;

[0046] S5: Calculate Z SCM According to the nonlinear model in S4, the function expression of the nonlinear model is calculated. On the basis of the function expression of the nonlinear model, the remote sensing reflectance characteristic R value of different chlorophyll a concentration vertical distribution is combined to perform regression to obtain the optimal depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water. The nonlinear regression model is combined with the remote sensing reflectance characteristics of 412nm, 555nm and 678nm caused by the vertical distribution of different chlorophyll a concentrations to invert the depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water. It can effectively include the optical characteristic signals caused by the vertical distribution of different chlorophyll a concentrations, and can independently rely on the remote sensing reflectance data obtained by satellite remote sensing to simply and quickly invert the depth of the maximum chlorophyll a concentration in the subsurface layer of the ocean water. The calculation amount is small, which reduces the dependence of machine learning on sea surface chlorophyll a concentration, sea surface temperature data, and sea surface height data, and reduces the computing resource consumption of running machine learning.

[0047] In the present invention, the R value is the ratio of the remote sensing reflectance at 412 nm, 555 nm and 678 nm after simple calculation. The calculation formula of the R value is: The functional expression of the nonlinear statistical model is: SCM =a0+a1X+a2X 2, replacing the unknown number X with the remote sensing reflectivity characteristic function R of the 412nm, 555nm, and 678nm bands, the functional expression of the obtained nonlinear statistical model is finally: The Z SCM It is obtained by fitting the global BGC-Argo chlorophyll a concentration profile using a Gaussian kernel function. SCM The specific fitting method is Where chla0 is the sea surface chlorophyll a concentration, σ is the variance of chlorophyll a concentration (4σ is the Gaussian peak width), h is the total chlorophyll a concentration within the Gaussian peak width, and Z SCM is the depth of the maximum profile concentration, Z is the water depth, a0, a1, a2 are the coefficients obtained by pairing the global BGC-Argo chlorophyll a concentration profile with the remote sensing reflectance spectral characteristic R value and performing nonlinear fitting, and Z is obtained by BGC-Argo. SCM The corresponding R value data is obtained by matching the global R value for the corresponding longitude and latitude and time using the BGC-Argo longitude and latitude data. A scatter plot comparing the subsurface chlorophyll-a maximum depth obtained by the proposed method for inverting the depth of subsurface chlorophyll-a concentration in ocean waters based on remote sensing reflectivity with the subsurface chlorophyll-a maximum depth obtained from global BGC-Argo data shows that this method is effective in inverting the global subsurface chlorophyll-a maximum depth, with a MAE of 10.87, a coefficient of determination (R2) of 0.85, and an RMSE of 13.75. The scatter plot distribution is concentrated near the y=x function, indicating high inversion accuracy. The calculation time on a typical desktop computer platform is only 10 seconds, significantly reducing the computational effort compared to the machine learning-based subsurface chlorophyll-a maximum depth inversion method, effectively reducing the computational resource consumption and difficulty of inverting the subsurface chlorophyll-a maximum.

[0048] In the description herein, it should be noted that relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "include," "comprise," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity, characterized in that: The following steps are involved: S1: Select bands, obtain remote sensing reflectance products from MODIS remote sensing products, select remote sensing reflectance data of three bands: 412nm, 555nm, and 678nm, and perform preprocessing; S2: Calculate the R value, calculate the difference between the remote sensing reflectance of 412nm and 678nm bands, and then calculate the ratio of this difference to the remote sensing reflectance data of 555nm to obtain R; S3: Collect data, extract the global measured BGC-Argo chlorophyll a concentration profile data, use Gaussian function to fit the BGC-Argo chlorophyll a profile, and select the depth of the maximum chlorophyll a concentration as Z SCM , calculate the subsurface chlorophyll a maximum depth of each BGC-Argo data and record the longitude and latitude; S4: Construct a model, match the longitude and latitude data provided by the BGC-Argo float with the corresponding longitude and latitude MODIS remote sensing reflectance, and average the R values ​​calculated from the remote sensing reflectance data of the two grid points around the grid point obtained by the BGC-Argo longitude and latitude positioning as a set of data, and pair the global BGC-Argo data according to this step, using the averaged R value as the input feature and the subsurface chlorophyll a maximum depth as the output feature to obtain a matching data set, and use the obtained data set to match Z SCM Draw a scatter plot of the two with the R value to obtain a nonlinear model; S5: Calculate Z SCM According to the nonlinear model in S4, the function expression of the nonlinear model is calculated. On the basis of the function expression of the nonlinear model, the remote sensing reflectance characteristic R value of the vertical distribution of different chlorophyll a concentrations is combined to perform regression to obtain the optimal maximum depth of chlorophyll a concentration in the subsurface layer of the ocean water body.

2. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 1 is characterized in that: The R value is the ratio of the remote sensing reflectances at 412nm, 555nm and 678nm after simple calculation.

3. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 2 is characterized in that: The calculation formula of the R value is:

4. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 1 is characterized in that: The functional expression of the nonlinear statistical model is: SCM =a0+a1X+a2X 2 .

5. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 4 is characterized in that: The unknown number X is replaced by the remote sensing reflectivity characteristic function R of the 412nm, 555nm, and 678nm bands, and the functional expression of the obtained nonlinear statistical model is finally:

6. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 5, characterized in that: The Z SCM It is obtained by fitting the global BGC-Argo chlorophyll a concentration profile using a Gaussian kernel function. SCM The specific fitting method is Where Chla0 is the sea surface chlorophyll a concentration, σ is the variance of chlorophyll a concentration (4σ is the Gaussian peak width), h is the total chlorophyll a concentration within the Gaussian peak width, and Z SCM is the depth of maximum concentration in the profile, and Z is the water depth.

7. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 6, characterized in that: The a0, a1, and a2 are coefficients obtained by pairing the global BGC-Argo chlorophyll a concentration profile with the remote sensing reflectance spectral characteristic R value and performing nonlinear fitting.

8. The method for inverting the maximum depth of chlorophyll a concentration in the subsurface layer of ocean water based on remote sensing reflectivity according to claim 7, characterized in that: The Z SCM The corresponding R value data is obtained by searching the longitude and latitude data of BGC-Argo for the R value of the corresponding longitude and latitude and time around the world and matching them.

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