A shallow sea multi-band bottom reflectivity remote sensing detection method and system

Abstract

The present invention provides a method and system for remote sensing detection of shallow-water multi-band bottom reflectance. This method combines four-band multispectral data and ICESat‑2 laser bathymetry data. By selecting characteristic points and iteratively searching for the optical parameters of the water body, the method uses the exponential relationship between the semi-analytical bottom reflectance and the spectral parameters to globally constrain the radiation transfer model, and constructs a quadratic polynomial model between the chlorophyll concentration of the characteristic points and the remote sensing reflectance of the blue and green bands, achieving large-scale rapid inversion of the optimal water body attenuation coefficient; further combined with the surface remote sensing reflectance product, a climatological monthly average deep-water reflectance data set is constructed, achieving large-scale and efficient detection of optical shallow-water multi-band bottom reflectance without the support of measured data. The present invention improves the application efficiency of four-band multispectral data, which has the richest historical data. The method is highly practical and has important significance for the protection and management of the benthic ecology of shallow-water coral reefs.

Description

Technical Field

[0001] The invention belongs to the field of remote sensing technology application and shallow sea coral reef benthic habitat monitoring, and relates to a shallow sea multi-band bottom reflectivity remote sensing detection method and system. Background Art

[0002] Shallow coral reef ecosystems are among the most productive marine ecosystems on Earth, crucial for maintaining marine ecological balance and sustainable resource utilization. However, coral bleaching events are becoming frequent due to global climate change and human disturbance. Traditional coral reef ecosystem surveys typically rely on ship-based measurements, submersible observations, and manual sampling. While these methods can provide accurate local information, they are costly, time-consuming, and have limited coverage, making large-scale, long-term monitoring difficult. Remote sensing technology, with its advantages of large-scale, non-invasive coverage, and high temporal and spatial resolution, enables long-term, large-scale monitoring of benthic habitats on remote marine islands and reefs, and has become an important tool for monitoring marine benthic ecosystems. Remote sensing mapping of coral reefs typically focuses on identifying coral reef distribution and landform types, such as using supervised / unsupervised classification, object-based training, and machine learning approaches to map bottom types. However, more refined, quantitative inversion of multi-band bottom reflectance that can characterize the composition and health of coral reef ecosystems remains limited.

[0003] The classic radiation transfer model is designed for hyperspectral remote sensing data and can simultaneously obtain bottom reflectance and water attenuation information. However, it is limited by the spatial resolution, data availability and computational efficiency of hyperspectral images, and its application in large-scale, precise and rapid detection is subject to certain restrictions. To this end, researchers have carried out a series of improvements based on multispectral data. However, the current quantitative detection methods of bottom reflectance based on multispectral data are mostly based on multispectral data with more than 8 bands, or based on single-band bottom reflectance quantitative detection based on four-band multispectral data, or introduce prior knowledge of the measured bottom to invert the bottom reflectance in a pixel-by-pixel optimization manner. How to effectively strip off the water column attenuation signal based on four-band multispectral data to achieve efficient and accurate recovery of multi-band bottom reflectance without relying on measured data remains an important challenge. Summary of the Invention

[0004] To solve the above problems, the present invention addresses the need for shallow-water coral reef bottom environment detection and proposes a shallow-water multi-band bottom reflectivity remote sensing detection method and system using four-band multispectral remote sensing images and ICESat-2 laser water depth data.

[0005] The technical solution adopted in the present invention is as follows:

[0006] A shallow sea multi-band bottom reflectivity remote sensing detection method comprises the following steps:

[0007] (1) Determine the study area and its adjacent deep-water area (a 500 × 500 m rectangular area approximately 2 km vertically outward from the study area), obtain four-band multispectral remote sensing images and ICESat-2 laser bathymetry data of the study area and its adjacent deep-water area, and obtain daily surface reflectance products of the adjacent deep-water area;

[0008] (2) Calculate the water column reflectance of the adjacent deep water area based on the daily surface reflectance product; process the four-band multispectral remote sensing image to obtain the four-band remote sensing reflectance; process the ICESat-2 laser bathymetry data to obtain the water depth grid of the study area;

[0009] (3) Randomly generate several feature points within the water depth grid of the study area;

[0010] (4) Search and iterate the optical parameters of the water body, calculate the 550nm bottom reflectance of the feature point and its correlation coefficient with the logarithmic rotation ratio of the remote sensing reflectance of the blue and green bands, and obtain the chlorophyll concentration and water body optical parameters of the feature point;

[0011] (5) Construct a quadratic polynomial function model of chlorophyll concentration and the logarithmic values ​​of blue and green band remote sensing reflectance, and train it to obtain a chlorophyll concentration inversion model;

[0012] (6) Calculate the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model, and calculate the multi-band bottom reflectance in combination with the water column reflectance.

[0013] Furthermore, the daily surface reflectivity product includes daily surface reflectivity data of the adjacent deep water area for several years; the calculation of the water column reflectivity of the adjacent deep water area based on the daily surface reflectivity product specifically includes: for the adjacent deep water area, respectively counting the mean and standard deviation of the surface reflectivity data of each month in all years, removing data points falling outside the range of ±2 times the standard deviation of the monthly mean for each month, and recalculating the mean of the remaining data as the water column reflectivity of that month.

[0014] Furthermore, the processing of the four-band multispectral remote sensing image includes radiation correction, glare correction and atmospheric correction; the specific steps of the atmospheric correction are: statistically calculating the mean of each band of the multispectral remote sensing image of the adjacent deep water area and the difference between the mean and the water column reflectance of the month. As the contribution of the atmosphere in each band image, all pixels in the entire image are uniformly subtracted Complete atmospheric correction.

[0015] Furthermore, step (4) specifically includes:

[0016] 4.1) Set the initial values, search range, and step size of the water optical parameters G and X to obtain m sets of G and X parameters, where G is the absorption coefficient of the yellow substance at 440 nm and X is the backscattering coefficient of the particle at 400 nm.

[0017] 4.2) Extract the four-band remote sensing reflectance and water depth of the feature points. Input the m groups of G and X parameters into the semi-analytical model in sequence to estimate the 550nm bottom reflectance and P parameter of the feature points. The resulting m groups of bottom reflectance and P parameters are obtained, where P is the absorption coefficient of phytoplankton at 440nm.

[0018] 4.3) Obtain the logarithmic values ​​of the blue and green bands of the feature points, and rotate the logarithmic values ​​of the blue and green bands about the center of gravity within the range of -90° to 90°. After rotation, obtain the logarithmic rotation ratio of the blue and green bands;

[0019] 4.4) For each set of 550nm substrate reflectance and P parameter, calculate the correlation coefficient between the logarithmic rotation ratio of the 550nm substrate reflectance and the blue and green band remote sensing reflectance at every 0.01° interval using the logarithmic rotation ratio model;

[0020] 4.5) Select the set of P parameters corresponding to the maximum correlation coefficient as the optimal P parameters of the feature point, and the corresponding set of G and X parameters as the optimal global water optical parameters. Convert the P parameters to chlorophyll concentration based on the empirical model.

[0021] Furthermore, step (5) specifically includes:

[0022] A quadratic polynomial chlorophyll concentration inversion model is constructed according to the chlorophyll concentration of the feature points and the logarithmic values ​​of the blue and green band remote sensing reflectances; and the chlorophyll concentration inversion model is trained to obtain model coefficients.

[0023] Furthermore, in step (6), the calculation of the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model specifically includes:

[0024] Using the trained chlorophyll concentration inversion model, the chlorophyll concentration of the entire target area is inverted pixel by pixel according to the logarithmic values ​​of the remote sensing reflectance of the blue and green bands, and then the optimal diffuse attenuation coefficient is calculated in combination with the global water optical parameters.

[0025] Furthermore, the multi-band bottom reflectivity is calculated using the following inversion model:

[0026]

[0027] in, is the remote sensing reflectance, is the water column reflectivity, is the optimal diffuse attenuation coefficient, For water depth, is the multi-band bottom reflectivity.

[0028] A shallow sea multi-band bottom reflectivity remote sensing detection system, comprising:

[0029] Data acquisition module: used to obtain four-band multispectral remote sensing images and ICESat-2 laser bathymetry data of the study area and its adjacent deep-water areas, as well as daily surface reflectance products of the adjacent deep-water areas;

[0030] Data processing module: used to calculate the water column reflectivity of the adjacent deep water area based on the daily surface reflectivity product; process the four-band multispectral remote sensing image to obtain the four-band remote sensing reflectivity; process the ICESat-2 laser bathymetry data to obtain the water depth grid of the study area;

[0031] Parameter calculation module: used to randomly generate a number of feature points within the water depth grid of the study area; search and iterate the optical parameters of the water body, calculate the correlation coefficient of the 550nm bottom reflectance of the feature point and the logarithmic rotation ratio of the blue and green band remote sensing reflectance, and obtain the chlorophyll concentration and water body optical parameters of the feature point;

[0032] Model inversion module: used to construct a quadratic polynomial function model of chlorophyll concentration and the logarithmic values ​​of blue and green band remote sensing reflectance, and perform training to obtain the chlorophyll concentration inversion model;

[0033] Reflectance calculation module: used to calculate the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model, and calculate the multi-band bottom reflectance in combination with the water column reflectance.

[0034] A computer device, comprising:

[0035] one or more processors;

[0036] a memory for storing one or more programs;

[0037] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned shallow sea multi-band bottom reflectivity remote sensing detection method.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned shallow sea multi-band bottom reflectivity remote sensing detection method.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] Optical remote sensing is a non-contact sensing method that receives signals from the reflection and scattering of sunlight. During this process, sunlight undergoes absorption and scattering as it passes through the atmosphere; reflection and refraction occur at the air-sea interface; and after entering the water, it undergoes absorption and scattering by the water before reaching the seafloor. After reflecting off the seafloor, the light undergoes absorption and scattering by the water, reflection and refraction at the air-sea interface, and absorption and scattering by the atmosphere before being received by the sensor and recorded as a multi-band optical remote sensing image. This radiation transmission process is extremely complex, and the signal that reaches the seafloor and returns to the sensor accounts for only a small portion of the overall spectrum. This signal contains key information such as bottom reflectivity and water depth, and can be used to detect bottom reflectivity and water parameters in shallow water.

[0041] Traditional observation methods such as ship-based surveys, diving observations, and manual sampling are limited by factors such as measurement cycles, personnel commitment, safety, and financial costs. Full coverage is difficult to achieve in many areas, and the update frequency is insufficient, making it difficult to meet the monitoring needs of shallow seabed conditions in oceanic islands and reefs. The emergence of remote sensing technology has provided new perspectives and means for regional monitoring of coral reef ecosystems, especially in terms of large-scale, efficient, and repeatable data acquisition. However, current multispectral remote sensing detection of bottom sediments focuses on bottom type mapping or single-band detection, or inefficient detection methods based on pixel-by-pixel optimization. There is currently a lack of a universal and efficient method for using the most abundant four-band multispectral data to strip optical shallow water water column attenuation signals and restore multi-band bottom reflectance.

[0042] Targeting shallow-water coral reef benthic ecological environment monitoring and resource management, this paper proposes an efficient remote sensing method for shallow-water multi-band bottom reflectance detection. Based on four-band multispectral data and ICESat-2 laser bathymetric data, the method selects characteristic points and iterates the search for water optical parameters G and X. This method introduces an exponential relationship between semi-analytical bottom reflectance and spectral parameters as a global constraint. A quadratic polynomial model is constructed that relates the chlorophyll concentration (Chl) at the characteristic points to the remote sensing reflectance of the blue and green bands. This model then calculates the optimal water diffuse attenuation coefficient pixel by pixel, improving the computational efficiency of the semi-analytical model while avoiding the occurrence of local optimal solutions. A monthly average climatological deep-water reflectance dataset is then constructed based on the daily MODIS surface reflectance product, enabling efficient multi-band bottom reflectance detection across the entire target area without the support of measured data. The present invention aims to address the limitations and inefficiency of the traditional semi-analytical detection model method for separating shallow seabed sedimension reflection and water column scattering signals, and helps to improve the application efficiency of four-band multispectral remote sensing data, which has the richest historical data. It is an innovation in the application of remote sensing information technology and a beneficial supplement to the efficient remote sensing detection method of shallow sea multi-band sediment reflectance, and has great practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flowchart of a shallow sea multi-band bottom reflectivity remote sensing detection method according to an embodiment of the present invention.

[0044] Figure 2 This is a three-dimensional display effect diagram of a quadratic polynomial model of the chlorophyll concentration of a feature point and the logarithmic values ​​of the remote sensing reflectance of the blue and green bands in an embodiment of the present invention.

[0045] Figure 3 It is an accuracy evaluation diagram of the quadratic polynomial model based on verification points in an embodiment of the present invention.

[0046] Figure 4 This is a diagram of the inversion results of the blue band bottom reflectivity of the study area in an embodiment of the present invention.

[0047] Figure 5 This is a diagram of the inversion results of the green band bottom reflectance of the study area in an embodiment of the present invention.

[0048] Figure 6 This is a diagram of the red band bottom reflectivity inversion result of the study area in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be further explained in detail below with reference to the accompanying drawings and specific examples.

[0050] A shallow sea multi-band bottom reflectivity remote sensing detection method comprises the following steps:

[0051] (1) Determine the study area and its adjacent deep-water area (a 500 × 500 m rectangular area approximately 2 km vertically outward from the study area), obtain four-band multispectral remote sensing images and ICESat-2 laser bathymetry data of the study area and its adjacent deep-water area, and obtain the daily surface reflectance product of the adjacent deep-water area.

[0052] (2) Calculate the water column reflectivity of the adjacent deep water area based on the daily surface reflectivity product; process the four-band multispectral remote sensing image to obtain the four-band remote sensing reflectivity; and process the ICESat-2 laser bathymetry data to obtain the water depth grid of the study area.

[0053] The surface reflectivity daily product includes daily surface reflectivity data of the adjacent deep-water area for several years; the calculation of water column reflectivity based on the surface reflectivity daily product specifically includes: for the adjacent deep-water area, respectively counting the mean and standard deviation of the surface reflectivity data of each month in all years, removing data points that fall outside the range of ±2 times the standard deviation of the monthly mean, and recalculating the mean of the remaining data as the water column reflectivity of that month.

[0054] The processing of the four-band multispectral remote sensing image includes radiation correction, glare correction and atmospheric correction; the specific steps of the atmospheric correction are: calculating the mean of each band of the four-band multispectral remote sensing image of the adjacent deep water area, subtracting the mean from the water column reflectance of the month, and calculating the difference. As the contribution of the atmosphere in each band image, all pixels in the entire image are uniformly subtracted from the difference. Complete atmospheric correction, where is the wavelength.

[0055] (3) Randomly generate several feature points within the water depth grid of the study area.

[0056] (4) Search and iterate the optical parameters of the water body, calculate the correlation coefficient of the logarithmic rotation ratio of the 550nm bottom reflectance of the feature point and the remote sensing reflectance of the blue and green bands, and obtain the chlorophyll concentration and water body optical parameters of the feature point. Specifically including:

[0057] 4.1) Set the initial values, search range, and step size of the water optical parameters G and X to obtain m sets of G and X parameters, where G is the absorption coefficient of the yellow substance at 440 nm and X is the backscattering coefficient of the particle at 400 nm.

[0058] 4.2) Extract the four-band remote sensing reflectance and water depth of the feature points. Input m sets of G and X parameters into the semi-analytical model in sequence to estimate the 550nm bottom reflectance and P parameter of the feature points. The resulting m sets of bottom reflectance and P parameters are obtained, where P is the absorption coefficient of phytoplankton at 440nm.

[0059] 4.3) Obtain the logarithmic values ​​of the blue and green bands of the feature points, and rotate the logarithmic values ​​of the blue and green bands about the center of gravity within the range of -90° to 90°. After rotation, obtain the logarithmic rotation ratio of the blue and green bands;

[0060] 4.4) For each set of substrate reflectance and P parameters, calculate the correlation coefficient between the logarithmic rotation ratio of the 550nm substrate reflectance and the blue and green band remote sensing reflectance at every 0.01° interval using the logarithmic rotation ratio model;

[0061] 4.5) Select the set of P parameters corresponding to the maximum correlation coefficient as the optimal P parameters for the feature point, and the corresponding set of G and X parameters as the optimal global water optical parameters; convert them into pixel-by-pixel chlorophyll concentration based on the empirical model.

[0062] (5) Construct a quadratic polynomial function model of chlorophyll concentration and the logarithmic values ​​of blue and green band remote sensing reflectance, and perform training to obtain a chlorophyll concentration inversion model. Specifically including:

[0063] A quadratic polynomial chlorophyll concentration inversion model is constructed according to the chlorophyll concentration of the feature points and the logarithmic values ​​of the blue and green band remote sensing reflectances; and the chlorophyll concentration inversion model is trained to obtain model coefficients.

[0064] The model formula is:

[0065]

[0066] Where Chl is the chlorophyll concentration of the characteristic point SIPs, -f are the coefficients of the quadratic polynomial function obtained by least squares fitting, and are the remote sensing reflectances of the blue and green bands, respectively. n is fixed to 10000 to ensure that the logarithm is a positive number.

[0067] (6) Calculate the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model, and calculate the multi-band bottom reflectance in combination with the water column reflectance.

[0068] The optimal diffuse attenuation coefficient is calculated by using the trained chlorophyll concentration inversion model, inverting the chlorophyll concentration of the entire target area pixel by pixel based on the logarithmic values ​​of the blue and green band remote sensing reflectance, and then combining the global water optical parameters to calculate the optimal diffuse attenuation coefficient using the following model:

[0069]

[0070] in, and are the absorption and scattering coefficients of pure water respectively, Chl is the chlorophyll concentration obtained above, and are all empirical parameters, S and Y are the spectral slope and spectral shape parameters respectively, is the diffuse attenuation coefficient, G is the absorption coefficient of the yellow substance at 440nm, It is based on e, Calculates an exponential raised to a power.

[0071] The multi-band bottom reflectivity is calculated using the following inversion model:

[0072]

[0073] in, is the remote sensing reflectance, is the water column reflectivity, is the optimal diffuse attenuation coefficient, For water depth, is the multi-band bottom reflectivity.

[0074] According to the present invention, a shallow sea multi-band bottom reflectivity remote sensing detection method is used for experiments. The technical route is as follows: Figure 1 As shown, this embodiment specifically includes the following steps:

[0075] (1) Generate a 500×500m dark pixel area in the deep water area near the study area, obtain the MODIS-Terra surface reflectance daily product in this area from 2000 to 2022, calculate the mean and standard deviation of August (the same month as the image formation) over the past 23 years, and remove the mean. For data outside the range of 2 times the standard deviation, the mean is recalculated as the water column reflectivity of the deep water area adjacent to the study area. , to eliminate outliers caused by factors such as clouds and ships.

[0076] (2) Radiation correction, glare correction and atmospheric correction were performed on the GF-2 image. The radiation correction was completed using ENVI5.3 remote sensing image processing software. The glare correction was based on the linear relationship between the visible light band and the near-infrared band to remove the specular reflection of sunlight on the sea surface. The calculation formula is:

[0077]

[0078] in, and Represent the reflectivity before and after flare correction, is the near-infrared reflectivity before glare correction, is the minimum reflectivity in the near-infrared band, is the fitting coefficient of the near-infrared band and each visible light band.

[0079] According to the water column reflectivity of the deep water area adjacent to the study area in step (1), Perform atmospheric correction and refraction correction on the image after glare correction in step (2) to obtain the remote sensing reflectivity below the subsurface The specific steps of atmospheric correction are: statistically calculate the mean of each band of the multispectral remote sensing image of the adjacent deep water area after glare correction, and make a difference with the water column reflectivity of the month to obtain the difference , as the contribution of the atmosphere in each band image, all pixels in the entire image are uniformly subtracted Atmospheric correction is completed. The laser satellite bathymetric data are preprocessed and fused with the corrected multispectral data to obtain the shallow water depth grid H for the study area. This method can be referenced (Han, T., Zhang, H., Cao, W., Le, C., Wang, C., Yang, X., Ma, Y., Li, D., Wang, J., Lou, X., 2023. Cost-efficient bathymetric mapping method based on massive active–passive remote sensing data. ISPRS Journal of Photogrammetry and Remote Sensing, 203, 285-300).

[0080] (3) Generate n random points (20≤n≤300) uniformly within the water depth grid in step (2) as feature points SIP[i] (i = 1, 2, 3, …, n), and extract the blue, green, and red band reflectance values ​​and water depth H of the corresponding spatial positions.

[0081] (4) Estimate the chlorophyll concentration Chl of the feature point SIP[i] generated in step (3) and the global water optical parameters G, X in the study area, where Chl varies with space, G, X are spatial constants, where G is the absorption coefficient of yellow matter at 440nm and X is the backscattering coefficient of suspended particles at 400nm. The specific estimation steps are as follows:

[0082] parameter Initial value Search Scope step length Number G 0.005 0.005-0.03 0.002 11 X 0.003 0.003-0.03 0.002 14

[0083] The smaller the step size and the greater the number, the higher the accuracy, but at the same time the amount of calculation will increase, so it can be set according to the specific situation.

[0084] 2) Read the four-band remote sensing reflectance and water depth of the feature point SIP[i], input m groups of different G, X parameter combinations in sequence, use the semi-analytical model to estimate the 550nm bottom reflectance B[i] and the 440nm absorption coefficient P[i] of SIP[i], and convert P[i] into chlorophyll concentration Chl[i] to obtain m groups of 550nm bottom reflectance B[i] and chlorophyll concentration Chl[i]. The idea behind the semi-analytical model estimation is as follows: for a given set of G and X parameters and the water depth at each characteristic point, the sensor's observation angle and solar angle at each characteristic point are combined to simulate the complex radiation transmission process of sunlight in optically shallow waters, obtain a simulated sea surface reflectance spectrum, and construct an error equation between the simulated spectrum and the satellite multispectral remote sensing reflectance. Using nonlinear optimization techniques, when the error equation reaches the minimum value, the corresponding 550nm bottom reflectance and chlorophyll concentration are the 550nm bottom reflectance and 440nm phytoplankton absorption coefficient P[i] obtained for this set of G and X combinations.

[0085] 3) Extract the logarithmic values ​​of B[i] and the blue and green band remote sensing reflectance obtained in step (3) from the m groups B[i] and P[i] in turn , , the scattered points ( , ) is rotated around the center of gravity in the range of -90°-90°, and after rotation, it is ( , ), according to the logarithmic rotation ratio model , calculate a B and a The correlation coefficient of the maximum correlation coefficient R 2 And the group of G, X parameters and corresponding P parameters, the maximum correlation coefficient R 2 The corresponding P parameter is used as the optimal P parameter of the feature point. The chlorophyll concentration of the feature point can be obtained according to the optimal P parameter. The G and X parameters corresponding to the maximum correlation coefficient are used as the global water optical parameters for the subsequent chlorophyll concentration Chl inversion model construction and the optimal diffuse attenuation coefficient. calculate.

[0086] (5) Chlorophyll concentration Chl[i] and logarithmic values ​​of blue and green band remote sensing reflectance of feature point SIP[i] obtained in step (4) , , randomly select 80% of the feature points to build a quadratic polynomial function inversion model, the model formula is:

[0087]

[0088] Where Chl is the chlorophyll concentration of the characteristic point SIPs, and af are the coefficients of the quadratic polynomial function obtained by least squares fitting, as shown in Figure 2 The following is a three-dimensional display of the quadratic polynomial model, where the dependent variable Chl and the two independent variables , showed a strong correlation. Figure 3 The accuracy evaluation of the model based on the remaining 20% ​​feature points is 0.0272mg / m 3 , the relative error is less than 2%.

[0089] (6) Apply the coefficient af obtained in step (5) to the logarithmic values ​​of the blue and green band remote sensing reflectance of the entire study area, and combine it with the global water optical parameters G, X obtained in step (4) to invert the optimal diffuse attenuation coefficient of the entire study area pixel by pixel. , combined with the water column reflectivity in step (1) , the multi-band bottom reflectivity is obtained based on the following model inversion . Figure 4 -Figure 6 shows the inversion results of bottom reflectance in the blue, green and red bands of the study area.

[0090]

[0091] After the above technical process, we finally successfully completed the quantitative and efficient remote sensing detection of multi-band bottom reflectance in the shallow sea of ​​the target area.

[0092] The present invention first introduces the semi-analytical bottom reflectance B[i] and the logarithmic rotation ratio of the blue-green band by selecting representative feature points SIP and searching the iterative water optical parameters G, X. [i] as a global constraint, a quadratic polynomial model of chlorophyll a concentration Chl and the logarithmic values ​​of blue and green band reflectance was creatively proposed, which overcame the defect of small amount of information in four-band multispectral remote sensing images and improved the optimization efficiency of the semi-analytical model; then, a monthly average dataset of climatological deep water reflectance was constructed through the MODIS surface reflectance daily product, which realized the efficient detection of multi-band bottom reflectance in a large range of shallow seas, and effectively solved the problem of bottom reflectance recovery based on four-band multispectral data in optical shallow water areas where bottom reflection and water column scattering are highly coupled and there is no control of field measured data.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A shallow sea multi-band bottom reflectivity remote sensing detection method, characterized in that: The following steps are involved: (1) Determine the study area and its adjacent deep-water areas, obtain four-band multispectral remote sensing images and ICESat-2 laser bathymetry data of the study area and its adjacent deep-water areas, and obtain daily surface reflectance products of the adjacent deep-water areas; (2) Calculate the water column reflectance of the adjacent deep water area based on the daily surface reflectance product; process the four-band multispectral remote sensing image to obtain the four-band remote sensing reflectance; process the ICESat-2 laser bathymetry data to obtain the water depth grid of the study area; (3) Randomly generate several feature points within the water depth grid of the study area; (4) Search and iterate the water optical parameters, calculate the 550nm bottom reflectance of the feature point and its correlation coefficient with the logarithmic rotation ratio of the remote sensing reflectance of the blue and green bands, and obtain the chlorophyll concentration of the feature point and the global water optical parameters; (5) Construct a quadratic polynomial function model of chlorophyll concentration and the logarithmic values ​​of blue and green band remote sensing reflectance, and train it to obtain a chlorophyll concentration inversion model; (6) Calculate the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model, and calculate the multi-band bottom reflectance in combination with the water column reflectance; The surface reflectivity daily product includes daily surface reflectivity data of the adjacent deep water area for several years; the calculation of the water column reflectivity of the adjacent deep water area based on the surface reflectivity daily product specifically includes: for the adjacent deep water area, respectively counting the mean and standard deviation of the surface reflectivity data of each month in all years, removing data points that fall outside the range of ±2 times the standard deviation of the mean of each month, and recalculating the mean of the remaining data as the water column reflectivity of that month.

2. The shallow sea multi-band bottom reflectivity remote sensing detection method according to claim 1, characterized in that: The processing of the four-band multispectral remote sensing image includes radiation correction, glare correction and atmospheric correction; the specific steps of the atmospheric correction are: calculating the mean of each band of the four-band multispectral remote sensing image of the adjacent deep water area, subtracting the mean from the water column reflectance of the current month, and uniformly subtracting the difference from all pixels of the entire image to complete the atmospheric correction.

3. The shallow sea multi-band bottom reflectivity remote sensing detection method according to claim 1, characterized in that: Step (4) specifically includes: 4.1) Set the initial values, search range, and step size of the water optical parameters G and X to obtain m sets of G and X parameters, where G is the absorption coefficient of the yellow substance at 440 nm and X is the backscattering coefficient of the particle at 400 nm. 4.2) Extract the four-band remote sensing reflectance and water depth of the feature points. Input the m groups of G and X parameters into the semi-analytical model in sequence to estimate the 550nm bottom reflectance and the 440nm absorption coefficient P of the phytoplankton at the feature points. The bottom reflectance and P parameters of the m groups of feature points are obtained. 4.3) Obtain the logarithmic values ​​of the blue and green bands of the feature points, and rotate the logarithmic values ​​of the blue and green bands about the center of gravity within the range of -90° to 90°. After rotation, obtain the logarithmic rotation ratio of the blue and green bands; 4.4) For each set of 550nm substrate reflectance and P parameter, calculate the correlation coefficient between the logarithmic rotation ratio of the 550nm substrate reflectance and the blue and green band remote sensing reflectance at every 0.01° interval using the logarithmic rotation ratio model; 4.5) Select the set of P parameters corresponding to the maximum correlation coefficient as the optimal P parameters of the feature point, and the corresponding set of G and X parameters as the optimal global water optical parameters. Convert the P parameters to chlorophyll concentration based on the empirical model.

4. The shallow sea multi-band bottom reflectivity remote sensing detection method according to claim 1, characterized in that: Step (5) specifically includes: A quadratic polynomial chlorophyll concentration inversion model is constructed according to the chlorophyll concentration of the feature points and the logarithmic values ​​of the blue and green band remote sensing reflectances; and the chlorophyll concentration inversion model is trained to obtain model coefficients.

5. The shallow sea multi-band bottom reflectivity remote sensing detection method according to claim 4, characterized in that: In step (6), the calculation of the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model specifically includes: Using the trained chlorophyll concentration inversion model, the chlorophyll concentration of the entire target area is inverted pixel by pixel according to the logarithm of the remote sensing reflectance of the blue and green bands. Then, combined with the global water optical parameters G and X, the optimal diffuse attenuation coefficient is calculated.

6. The shallow sea multi-band bottom reflectivity remote sensing detection method according to claim 5, characterized in that: In step (6), the multi-band bottom reflectivity is calculated using the following inversion model: ， in, is the remote sensing reflectance, is the water column reflectivity, is the optimal diffuse attenuation coefficient, For water depth, is the multi-band bottom reflectivity.

7. A shallow sea multi-band bottom reflectivity remote sensing detection system, characterized in that: Used to implement the method according to any one of claims 1 to 6, comprising: Data acquisition module: used to obtain four-band multispectral remote sensing images and ICESat-2 laser bathymetry data of the study area and its adjacent deep-water areas, as well as daily surface reflectance products of the adjacent deep-water areas; Data processing module: used to calculate the water column reflectivity of the adjacent deep water area based on the daily surface reflectivity product; process the four-band multispectral remote sensing image to obtain the four-band remote sensing reflectivity; process the ICESat-2 laser bathymetry data to obtain the water depth grid of the study area; Parameter calculation module: used to randomly generate a number of feature points within the water depth grid of the study area; search and iterate the optical parameters of the water body, calculate the correlation coefficient of the 550nm bottom reflectance of the feature point and the logarithmic rotation ratio of the blue and green band remote sensing reflectance, and obtain the chlorophyll concentration and water body optical parameters of the feature point; Model inversion module: used to construct a quadratic polynomial function model of chlorophyll concentration and the logarithmic values ​​of blue and green band remote sensing reflectance, and perform training to obtain the chlorophyll concentration inversion model; Reflectance calculation module: used to calculate the optimal diffuse attenuation coefficient pixel by pixel based on the chlorophyll concentration inversion model, and calculate the multi-band bottom reflectance in combination with the water column reflectance.

8. A computer device, characterized in that: The computer device comprises: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the shallow sea multi-band bottom reflectivity remote sensing detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the shallow sea multi-band bottom reflectivity remote sensing detection method as described in any one of claims 1 to 6 is implemented.

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