Method and System for Inverting Chlorophyll-a Concentration in Water Bodies by Hyperspectral Satellites

Abstract

The present invention relates to the field of hyperspectral satellite technology, and is a method and system for retrieving the chlorophyll-a concentration of water bodies by a hyperspectral satellite. The method includes: obtaining the remote sensing reflectance data of sampling points in the background dataset of lake and reservoir water bodies, and calculating the equivalent remote sensing reflectance of the sampling points in the hyperspectral satellite data bands; calculating the average remote sensing reflectance spectra of different water body types in each preset band according to the equivalent remote sensing reflectance of the sampling points; determining the chlorophyll-a inversion model corresponding to each water body type; calculating the spectral angle cosine of the average remote sensing reflectance spectra corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image in the preset band respectively to determine the target water body type to which the water body pixels belong; and using the chlorophyll-a inversion model corresponding to the target water body type to determine the target chlorophyll-a concentration corresponding to the water body pixels in the hyperspectral image. Through this solution, the calculation accuracy of the chlorophyll-a concentration of water bodies from satellite hyperspectral data is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of hyperspectral satellite technology, and in particular, to a method and system for retrieving chlorophyll a concentration in water bodies of a hyperspectral satellite. Background Art

[0002] Chlorophyll a concentration is an important indicator for water environment monitoring. Currently, the monitoring methods for chlorophyll a concentration include on-site direct measurement and remote sensing indirect measurement. On-site direct measurement is to collect water samples in the field and store them at low temperature, and then analyze them in the laboratory to obtain the chlorophyll a concentration of the water body at the sampling point. Since the sampling points and sampling times of manual measurement are limited, this method cannot comprehensively evaluate the spatial distribution of chlorophyll a in water bodies. Remote sensing indirect measurement is to find the relationship between the water body spectral characteristics and chlorophyll a concentration, and establish a model for inversion to obtain the latter's result.

[0003] Due to the differences in data quality (remote sensing reflectance accuracy) and payload indicators (band settings, spatial resolution) between Chinese satellite hyperspectral data and foreign water color satellite data such as SeaWIFS, MODIS, and MERIS, the existing technologies cannot obtain good chlorophyll a concentration inversion results when directly applied to domestic hyperspectral satellites. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, the present disclosure provides a method and system for retrieving chlorophyll a concentration in water bodies of a hyperspectral satellite.

[0005] According to the first aspect of the embodiments of the present disclosure, a method for retrieving chlorophyll a concentration in water bodies of a hyperspectral satellite is provided, including:

[0006] Obtain the remote sensing reflectance data of sampling points in the lake / reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data;

[0007] According to the equivalent remote sensing reflectance of the sampling points, respectively determine the average remote sensing reflectance spectra of different water body types in each preset band, where the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type;

[0008] Determine the chlorophyll a inversion model corresponding to each water body type;

[0009] Respectively calculate the spectral angle cosine between the average remote sensing reflectance spectra corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image, so as to determine the target water body type to which the water body pixels in the hyperspectral image belong;

[0010] Use the chlorophyll a inversion model corresponding to the target water body type to determine the target chlorophyll a concentration corresponding to the water body pixels in the hyperspectral image.

[0011] In one embodiment, preferably, the following first calculation formula is used to calculate the equivalent remote sensing reflectance of the water body in the hyperspectral satellite data band:

[0012]

[0013] Wherein, represents the equivalent remote sensing reflectance of the water body in the preset band of the hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i (λ) represents the spectral response function of the i-th preset band of the hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function;

[0014] The following second calculation formula is used to calculate the average remote sensing reflectance spectrum corresponding to different water body types in the preset band:

[0015]

[0016] Wherein, represents the average remote sensing reflectance of any water body type in the i-th hyperspectral satellite data band, and N is the number of samples in the lake / reservoir water body background data set of this water body type.

[0017] In one embodiment, preferably, determining the target water body type to which the water body pixel of the hyperspectral image belongs includes:

[0018] Obtaining the water quality data of the sampling points of each water body type in the lake / reservoir water body background data set, wherein the water quality data includes chlorophyll a concentration data;

[0019] According to the equivalent remote sensing reflectance of the water body and the chlorophyll a concentration data of each water body type, determine the chlorophyll a inversion model corresponding to each water body type.

[0020] In one embodiment, preferably, determining the target water body type to which the water body pixel of the hyperspectral image belongs includes:

[0021] When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixel of the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixel of the hyperspectral image belongs is this any water body type;

[0022] When the spectral angular cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water type in the preset band is less than the preset value, the weight value of the water pixels in the hyperspectral image relative to each water type is calculated respectively.

[0023] In one embodiment, preferably, the following third calculation formula is used to calculate the first weight value of the water pixels in the hyperspectral image relative to the oligotrophic water type:

[0024]

[0025] The following fourth calculation formula is used to calculate the second weight value of the water pixels in the hyperspectral image relative to the mesotrophic water type:

[0026]

[0027] The following fifth calculation formula is used to calculate the second weight value of the water pixels in the hyperspectral image relative to the eutrophic water type:

[0028]

[0029] Wherein, SA1 represents the spectral angular cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water type in the preset band, SA2 represents the spectral angular cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water type in the preset band, and SA3 represents the spectral angular cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water type in the preset band.

[0030] In one embodiment, preferably, using the chlorophyll a inversion model corresponding to the target water type to determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image includes:

[0031] When it is determined that the target water type to which the water pixels in the hyperspectral image belong is any water type, the chlorophyll a inversion model corresponding to the target water type is used to determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image;

[0032] When it is determined that the water pixels in the hyperspectral image do not belong to any water type, then according to the weight value of the water pixels in the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to each water type, the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image is determined.

[0033] In one embodiment, preferably, when it is determined that the water pixels of the hyperspectral image do not belong to any water type, the target chlorophyll a concentration corresponding to the water pixels of the hyperspectral image is determined according to the weight values of the water pixels of the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to the respective water types, including:

[0034] The following sixth calculation formula is used to calculate the target chlorophyll a concentration corresponding to the water pixels of the hyperspectral image:

[0035]

[0036] wherein, M1 represents the chlorophyll a inversion model corresponding to the oligotrophic water type, M2 represents the chlorophyll a inversion model corresponding to the mesotrophic water type, and M3 represents the chlorophyll a inversion model corresponding to the eutrophic water type.

[0037] According to the second aspect of the embodiments of the present disclosure, a device for inverting the chlorophyll a concentration of water bodies of a hyperspectral satellite is provided, including:

[0038] An acquisition module, configured to acquire the remote sensing reflectance data of the sampling points in the lake and reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data;

[0039] A first determination module, configured to respectively determine the average remote sensing reflectance spectra of different water types in each preset band according to the equivalent remote sensing reflectance of the sampling points, wherein the water types include: oligotrophic water type, mesotrophic water type, and eutrophic water type;

[0040] A second determination module, configured to determine the chlorophyll a inversion model corresponding to each water type;

[0041] A calculation module, configured to respectively calculate the spectral angle cosine between the average remote sensing reflectance spectra corresponding to the respective water types and the remote sensing reflectance of the water pixels of the hyperspectral image in the preset bands to determine the target water type to which the water pixels of the hyperspectral image belong;

[0042] A third determination module, configured to use the chlorophyll a inversion model corresponding to the target water type to determine the target chlorophyll a concentration corresponding to the water pixels of the hyperspectral image.

[0043] The following first calculation formula is used to calculate the equivalent remote sensing reflectance of the water body in the hyperspectral satellite data band:

[0044]

[0045] wherein, represents the equivalent remote sensing reflectance of the water body in the hyperspectral satellite data band, represents the remote sensing reflectance data of the water body, f i (λ) represents the spectral response function of the i-th hyperspectral satellite data band, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function;

[0046] The following second calculation formula is used to calculate the average remote sensing reflectance spectrum corresponding to different water body types in the preset band:

[0047]

[0048] where represents the average remote sensing reflectance of any water body type in the i-th hyperspectral satellite data band, and N is the number of samples in the lake / reservoir water body background data set of this water body type.

[0049] In one embodiment, preferably, the second determination module is used for:

[0050] Obtain the water quality data of each water body type in the lake / reservoir water body background data set, where the water quality data includes chlorophyll a concentration data;

[0051] Determine the chlorophyll a inversion model corresponding to each water body type according to the equivalent remote sensing reflectance of the water body and the chlorophyll a concentration data of each water body type.

[0052] In one embodiment, preferably, the calculation module is used for:

[0053] When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixel in the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixel in the hyperspectral image belongs is this any water body type;

[0054] When the spectral angle cosine of the remote sensing reflectance of the water body pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water body type in the preset band is less than the preset value, calculate the weight value of the water body pixel in the hyperspectral image relative to each water body type respectively.

[0055] In one embodiment, preferably, the following third calculation formula is used to calculate the first weight value of the water body pixel in the hyperspectral image relative to the oligotrophic water body type:

[0056]

[0057] The following fourth calculation formula is used to calculate the second weight value of the water body pixel in the hyperspectral image relative to the mesotrophic water body type:

[0058]

[0059] The second weight value of the water pixel of the hyperspectral image relative to the eutrophic water body type is calculated using the following fifth calculation formula:

[0060]

[0061] Wherein, SA1 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water body type in the preset band, SA2 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water body type in the preset band, and SA3 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water body type in the preset band.

[0062] In one embodiment, preferably, the third determination module is configured to:

[0063] When it is determined that the target water body type to which the water pixel of the hyperspectral image belongs is any water body type, the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image is determined by using the chlorophyll a inversion model corresponding to the target water body type;

[0064] When it is determined that the water pixel of the hyperspectral image does not belong to any water body type, the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image is determined according to the weight value of the water pixel of the hyperspectral image relative to each water body type and the chlorophyll a inversion models corresponding to each water body type.

[0065] In one embodiment, preferably, when it is determined that the water pixel of the hyperspectral image does not belong to any water body type, the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image is determined according to the weight value of the water pixel of the hyperspectral image relative to each water body type and the chlorophyll a inversion models corresponding to each water body type, including:

[0066] The target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image is calculated using the following sixth calculation formula:

[0067]

[0068] Wherein, M1 represents the chlorophyll a inversion model corresponding to the oligotrophic water body type, M2 represents the chlorophyll a inversion model corresponding to the mesotrophic water body type, and M3 represents the chlorophyll a inversion model corresponding to the eutrophic water body type.

[0069] According to a third aspect of the embodiments of the present disclosure, there is provided a device for retrieving the chlorophyll-a concentration of water bodies of a hyperspectral satellite, including:

[0070] A processor;

[0071] A memory for storing instructions executable by the processor;

[0072] Wherein, the processor is configured to:

[0073] Obtain the remote sensing reflectance data of sampling points in the water body background dataset of lakes and reservoirs, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data;

[0074] According to the equivalent remote sensing reflectance of the sampling points, respectively determine the average remote sensing reflectance spectra of different water body types in each preset band, wherein the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type;

[0075] Determine the chlorophyll-a inversion model corresponding to each water body type;

[0076] Respectively calculate the spectral angle cosine of the average remote sensing reflectance spectra corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image in the preset band to determine the target water body type to which the water body pixels in the hyperspectral image belong;

[0077] Use the chlorophyll-a inversion model corresponding to the target water body type to determine the target chlorophyll-a concentration corresponding to the water body pixels in the hyperspectral image.

[0078] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in any one of the embodiments of the first aspect are implemented.

[0079] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

[0080] In the embodiments of the present invention, based on the water body remote sensing reflectance and chlorophyll-a concentration collected on the ground, the best model for applying satellite hyperspectral data to chlorophyll-a concentration inversion is determined, and the inversion of the chlorophyll-a concentration of water bodies based on satellite hyperspectral data is realized, improving the calculation accuracy of the chlorophyll-a concentration of water bodies in satellite hyperspectral data.

[0081] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0083] Figure 1 It is a flowchart of a method for retrieving chlorophyll-a concentration in water bodies of a hyperspectral satellite shown according to an exemplary embodiment.

[0084] Figure 2 It is a flowchart of step S103 in a method for retrieving chlorophyll-a concentration in water bodies of a hyperspectral satellite shown according to an exemplary embodiment.

[0085] Figure 3 It is a block diagram of a device for retrieving chlorophyll-a concentration in water bodies of a hyperspectral satellite shown according to an exemplary embodiment. Detailed implementation manners

[0086] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0087] Figure 1 It is a flowchart of a method for retrieving chlorophyll-a concentration in water bodies of a hyperspectral satellite shown according to an exemplary embodiment.

[0088] As Figure 1 shown, the method for retrieving chlorophyll-a concentration in water bodies of a hyperspectral satellite includes:

[0089] Step S101, obtaining remote sensing reflectance data of sampling points in the lake / reservoir water body background dataset, and calculating the equivalent remote sensing reflectance of the sampling points in a preset band of hyperspectral satellite data;

[0090] In one embodiment, preferably, the following first calculation formula is used to calculate the equivalent remote sensing reflectance of the water body in the hyperspectral satellite data band:

[0091]

[0092] Wherein, represents the equivalent remote sensing reflectance of the water body in the preset band of hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i (λ) represents the spectral response function of the i-th preset band of hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function;

[0093] Step S102: Determine the average remote sensing reflectance spectra of different water body types in each preset band according to the equivalent remote sensing reflectance of the sampling points, where the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type;

[0094] Calculate the average remote sensing reflectance spectra of different water body types in each preset band by using the following second calculation formula:

[0095]

[0096] where represents the average remote sensing reflectance of any water body type in the preset band of the i-th hyperspectral satellite data, and N is the number of samples in the lake / reservoir water body background dataset of this water body type.

[0097] where the preset band includes the hyperspectral satellite data band of 400 - 720 nm.

[0098] Step S103: Determine the chlorophyll-a inversion model corresponding to each water body type;

[0099] As Figure 2 shown, in one embodiment, preferably, Step S103 includes:

[0100] Step S201: Obtain the water quality data of each water body type in the lake / reservoir water body background dataset, where the water quality data includes chlorophyll-a concentration data;

[0101] Step S202: Determine the chlorophyll-a inversion model corresponding to each water body type according to the equivalent remote sensing reflectance of the water body and the chlorophyll-a concentration data of each water body type.

[0102] For the chlorophyll-a concentration inversion of each water body type, it is recommended to use the following models respectively:

[0103] For the oligotrophic water body type, use an empirical model based on the blue-green band ratio.

[0104] M1 = 10^(a + bx + cx 2 + dx 3 + ex 4 )

[0105]

[0106] R rs (510) and R rs (555) represent the remote sensing reflectance of the hyperspectral image in the two bands of 510 nm and 555 nm, and a, b, c, d, e are model parameters to be determined.

[0107] For mesotrophic water body types, a semi-analytical model is adopted.

[0108] M2 = a ph (665) / 0.017

[0109] The semi-analytical model calculates the absorption coefficient aph at the 665 nm band through remote sensing reflectance, and then calculates the chlorophyll a concentration. If the semi-analytical model is adopted, no model parameters need to be determined.

[0110] For eutrophic water body types, an empirical model based on the ratio of near-infrared to red bands is adopted.

[0111]

[0112] R rs (705) and R rs (671) represent the remote sensing reflectance of the hyperspectral image at the two bands of 705 nm and 671 nm, and a and b are model parameters to be determined.

[0113] Step S104, calculate the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image at the preset band respectively, to determine the target water body type to which the water body pixels in the hyperspectral image belong;

[0114] In one embodiment, preferably, determining the target water body type to which the water body pixels in the hyperspectral image belong includes:

[0115] When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image at the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixels in the hyperspectral image belong is this any water body type;

[0116] When the spectral angle cosine of the remote sensing reflectance of the water body pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water body type at the preset band is less than the preset value, calculate the weight value of the water body pixels in the hyperspectral image relative to each water body type respectively.

[0117] In one embodiment, preferably, the following third calculation formula is adopted to calculate the first weight value of the water body pixels in the hyperspectral image relative to the oligotrophic water body type:

[0118]

[0119] The following fourth calculation formula is adopted to calculate the second weight value of the water body pixels in the hyperspectral image relative to the mesotrophic water body type:

[0120]

[0121] The second weight value of the water pixel of the hyperspectral image relative to the eutrophic water type is calculated using the following fifth calculation formula:

[0122]

[0123] Among them, SA1 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water type in the preset band, SA2 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water type in the preset band, and SA3 represents the spectral angle cosine of the remote sensing reflectance of the water pixel of the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water type in the preset band.

[0124] Step S105, using the chlorophyll a inversion model corresponding to the target water type, determine the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image.

[0125] In one embodiment, preferably, using the chlorophyll a inversion model corresponding to the target water type to determine the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image includes:

[0126] When it is determined that the target water type to which the water pixel of the hyperspectral image belongs is any water type, use the chlorophyll a inversion model corresponding to the target water type to determine the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image;

[0127] When it is determined that the water pixel of the hyperspectral image does not belong to any water type, then according to the weight value of the water pixel of the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to each water type, determine the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image.

[0128] In one embodiment, preferably, when it is determined that the water pixel of the hyperspectral image does not belong to any water type, then according to the weight value of the water pixel of the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to each water type, determine the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image, including:

[0129] The following sixth calculation formula is used to calculate the target chlorophyll a concentration corresponding to the water pixel of the hyperspectral image:

[0130]

[0131] Among them, M1 represents the chlorophyll a inversion model corresponding to the oligotrophic water body type, M2 represents the chlorophyll a inversion model corresponding to the mesotrophic water body type, and M3 represents the chlorophyll a inversion model corresponding to the eutrophic water body type.

[0132] Figure 3 It is a block diagram of a device for retrieving the chlorophyll a concentration in water bodies of a hyperspectral satellite shown according to an exemplary embodiment.

[0133] As Figure 3 shown, according to the second aspect of the embodiments of the present disclosure, there is provided a device for retrieving the chlorophyll a concentration in water bodies of a hyperspectral satellite, including:

[0134] An acquisition module, configured to acquire the remote sensing reflectance data of sampling points in the lake and reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in a preset band of the hyperspectral satellite data;

[0135] A first determination module, configured to respectively determine the average remote sensing reflectance spectra of different water body types in each preset band according to the equivalent remote sensing reflectance of the water body, where the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type;

[0136] A second determination module, configured to determine the chlorophyll a inversion model corresponding to each water body type;

[0137] A calculation module, configured to respectively calculate the spectral angle cosine between the average remote sensing reflectance spectrum corresponding to each water body type and the remote sensing reflectance of the water body pixel in the hyperspectral image in the preset band, so as to determine the target water body type to which the water body pixel in the hyperspectral image belongs;

[0138] A third determination module, configured to use the chlorophyll a inversion model corresponding to the target water body type to determine the target chlorophyll a concentration corresponding to the water body pixel in the hyperspectral image.

[0139] The following first calculation formula is used to calculate the equivalent remote sensing reflectance of the water body in the hyperspectral satellite data band:

[0140]

[0141] Among them, represents the equivalent remote sensing reflectance of the water body in the preset band of the hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i (λ) represents the spectral response function of the i-th preset band of the hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function;

[0142] The following second calculation formula is used to calculate the average remote sensing reflectance spectrum corresponding to different water body types in the preset band:

[0143]

[0144] Wherein, represents the average remote sensing reflectance of any water body type in the preset band of the i-th hyperspectral satellite data, and N is the number of samples in the lake / reservoir water body background data set of this water body type.

[0145] In one embodiment, preferably, the second determination module is configured to:

[0146] Obtain the water quality data of each water body type in the lake / reservoir water body background data set, wherein the water quality data includes chlorophyll a concentration data;

[0147] According to the equivalent remote sensing reflectance of the water body and the chlorophyll a concentration data of each water body type, determine the chlorophyll a inversion model corresponding to each water body type.

[0148] In one embodiment, preferably, the calculation module is configured to:

[0149] When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixel in the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixel in the hyperspectral image belongs is this any water body type;

[0150] When the spectral angle cosine of the remote sensing reflectance of the water body pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water body type in the preset band is less than the preset value, calculate the weight value of the water body pixel in the hyperspectral image relative to each water body type respectively.

[0151] In one embodiment, preferably, the following third calculation formula is used to calculate the first weight value of the water body pixel in the hyperspectral image relative to the oligotrophic water body type:

[0152]

[0153] The following fourth calculation formula is used to calculate the second weight value of the water body pixel in the hyperspectral image relative to the mesotrophic water body type:

[0154]

[0155] The following fifth calculation formula is used to calculate the second weight value of the water body pixel in the hyperspectral image relative to the eutrophic water body type:

[0156]

[0157] Among them, SA1 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water type in the preset band, SA2 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water type in the preset band, and SA3 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water type in the preset band.

[0158] In one embodiment, preferably, the third determination module is configured to:

[0159] When it is determined that the target water type to which the water pixels in the hyperspectral image belong is any water type, use the chlorophyll a inversion model corresponding to the target water type to determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image;

[0160] When it is determined that the water pixels in the hyperspectral image do not belong to any water type, then determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image according to the weight value of the water pixels in the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to each water type.

[0161] In one embodiment, preferably, when it is determined that the water pixels in the hyperspectral image do not belong to any water type, then determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image according to the weight value of the water pixels in the hyperspectral image relative to each water type and the chlorophyll a inversion models corresponding to each water type, including:

[0162] Use the following sixth calculation formula to calculate the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image:

[0163]

[0164] Among them, M1 represents the chlorophyll a inversion model corresponding to the oligotrophic water type, M2 represents the chlorophyll a inversion model corresponding to the mesotrophic water type, and M3 represents the chlorophyll a inversion model corresponding to the eutrophic water type.

[0165] According to the third aspect of the embodiments of the present disclosure, there is provided a device for inverting the chlorophyll a concentration of water bodies of a hyperspectral satellite, including:

[0166] A processor;

[0167] A memory for storing instructions executable by the processor;

[0168] Among them, the processor is configured to:

[0169] Obtain the remote sensing reflectance data of the sampling points in the background dataset of the lake and reservoir water bodies, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data;

[0170] According to the equivalent remote sensing reflectance of the sampling points, respectively determine the average remote sensing reflectance spectra corresponding to different water body types in the preset bands, where the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type;

[0171] Determine the chlorophyll a inversion model corresponding to each water body type;

[0172] Respectively calculate the spectral angle cosine of the average remote sensing reflectance spectra corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image in the preset bands to determine the target water body type to which the water body pixels in the hyperspectral image belong;

[0173] Adopt the chlorophyll a inversion model corresponding to the target water body type to determine the target chlorophyll a concentration corresponding to the water body pixels in the hyperspectral image.

[0174] According to the fourth aspect of the present invention, there is provided a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in any one of the embodiments of the first aspect are implemented.

[0175] It can be further understood that in the present disclosure, "a plurality of" means two or more, and other quantifiers are similar. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The singular forms of "a", "the", and "said" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0176] It can be further understood that the terms "first", "second", etc. are used to describe various information, but this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not represent a specific order or importance. In fact, the expressions "first", "second", etc. can be used interchangeably completely. For example, without departing from the scope of the present disclosure, the first information can also be called the second information, and similarly, the second information can also be called the first information.

[0177] It can be further understood that although operations are depicted in the drawings in a particular order in the embodiments of the present disclosure, it should not be construed as requiring that these operations be performed in the particular order shown or in a sequential order, or that all of the illustrated operations be performed to obtain the desired result. In certain circumstances, multitasking and parallel processing may be advantageous.

[0178] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0179] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for retrieving the concentration of chlorophyll a in water bodies of a hyperspectral satellite, characterized in that, Including: Obtain the remote sensing reflectance data of the sampling points in the lake / reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data. Use the following first calculation formula to calculate the equivalent remote sensing reflectance of the sampling points in the hyperspectral satellite data bands: Among them, represents the equivalent remote sensing reflectance of the water body in the preset band of the hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i (λ) represents the spectral response function of the preset band of the i-th hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function; According to the equivalent remote sensing reflectance of the sampling points, respectively determine the average remote sensing reflectance spectra of different water body types in each preset band. Use the following second calculation formula to calculate the average remote sensing reflectance spectra of different water body types in each preset band: Among them, represents the average remote sensing reflectance of any water body type in the preset band of the i-th hyperspectral satellite data. N is the number of samples in the background dataset of lake and reservoir water bodies of this water body type. Among them, the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type; Determine the chlorophyll-a inversion model corresponding to each water body type; Respectively calculate the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to each water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image in the preset band to determine the target water body type to which the water body pixels in the hyperspectral image belong. Determining the target water body type to which the water body pixels in the hyperspectral image belong includes: When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixels in the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixels in the hyperspectral image belong is this any water body type; When the spectral angle cosine of the remote sensing reflectance of the water body pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water body type in the preset band is less than the preset value, respectively calculate the weight value of the water body pixels in the hyperspectral image relative to each water body type. Use the following third calculation formula to calculate the first weight value of the water body pixels in the hyperspectral image relative to the oligotrophic water body type: Use the following fourth calculation formula to calculate the second weight value of the water body pixels in the hyperspectral image relative to the mesotrophic water body type: Use the following fifth calculation formula to calculate the second weight value of the water body pixels in the hyperspectral image relative to the eutrophic water body type: Wherein, SA1 represents the spectral angle cosine of the remote sensing reflectance of the water body pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water body type in the preset band, SA2 represents the spectral angle cosine of the remote sensing reflectance of the water body pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water body type in the preset band, and SA3 represents the spectral angle cosine of the remote sensing reflectance of the water body pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water body type in the preset band; Use the chlorophyll-a inversion model corresponding to the target water body type to determine the target chlorophyll-a concentration corresponding to the water body pixels in the hyperspectral image.

2. The method for retrieving the chlorophyll a concentration in water bodies of a hyperspectral satellite according to claim 1, characterized in that, Determining the chlorophyll-a inversion model corresponding to each water body type includes: Obtain the water quality data of the sampling points of each water body type in the lake / reservoir water body background dataset, wherein the water quality data includes chlorophyll-a concentration data; For each water body type, according to the equivalent remote sensing reflectance of the sampling points belonging to this type and the corresponding chlorophyll-a concentration data, determine the chlorophyll-a inversion model corresponding to each water body type.

3. The method for inverting the water chlorophyll a concentration of the hyperspectral satellite according to claim 1, wherein Using the chlorophyll a inversion model corresponding to the target water body type to determine the target chlorophyll a concentration corresponding to the water body pixel of the hyperspectral image, including: When it is determined that the target water body type to which the water body pixel of the hyperspectral image belongs is any water body type, use the chlorophyll a inversion model corresponding to the target water body type to determine the target chlorophyll a concentration corresponding to the water body pixel of the hyperspectral image; When it is determined that the water body pixel of the hyperspectral image does not belong to any water body type, then determine the target chlorophyll a concentration corresponding to the water body pixel of the hyperspectral image according to the weight value of the water body pixel of the hyperspectral image relative to each water body type and the chlorophyll a inversion models corresponding to each water body type.

4. The method for inverting the concentration of chlorophyll a in water bodies of a hyperspectral satellite according to claim 3, wherein When it is determined that the water body pixel of the hyperspectral image does not belong to any water body type, then determine the target chlorophyll a concentration corresponding to the water body pixel of the hyperspectral image according to the weight value of the water body pixel of the hyperspectral image relative to each water body type and the chlorophyll a inversion models corresponding to each water body type, including: Use the following sixth calculation formula to calculate the target chlorophyll a concentration corresponding to the water body pixel of the hyperspectral image: Wherein, M1 represents the chlorophyll a inversion model corresponding to the oligotrophic water body type, M2 represents the chlorophyll a inversion model corresponding to the mesotrophic water body type, and M3 represents the chlorophyll a inversion model corresponding to the eutrophic water body type.

5. An apparatus for retrieving water chlorophyll a concentration of a hyperspectral satellite, characterized in that, Including: An acquisition module, configured to acquire the remote sensing reflectance data of the sampling points in the lake reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in the preset bands of the hyperspectral satellite data. Use the following first calculation formula to calculate the equivalent remote sensing reflectance of the sampling points in the hyperspectral satellite data bands: Among them, represents the equivalent remote sensing reflectance of the water body in the preset band of the hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i f(λ) represents the spectral response function of the preset band of the i-th hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function; A first determination module, configured to respectively determine the average remote sensing reflectance spectra of different water body types in each preset band according to the equivalent remote sensing reflectance of the sampling points. Use the following second calculation formula to calculate the average remote sensing reflectance spectra of different water body types in each preset band: Among them, represents the average remote sensing reflectance of any water body type in the preset band of the i-th hyperspectral satellite data, and N is the number of samples in the lake and reservoir water body background dataset of this water body type. Among them, the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type; A second determination module, configured to determine the chlorophyll a inversion model corresponding to each water body type; A calculation module, configured to respectively calculate the spectral angle cosine between the average remote sensing reflectance spectra corresponding to each water body type and the remote sensing reflectance of the water body pixel of the hyperspectral image in the preset band, so as to determine the target water body type to which the water body pixel of the hyperspectral image belongs. Determining the target water body type to which the water body pixel of the hyperspectral image belongs includes: When the spectral angle cosine between the average remote sensing reflectance spectrum corresponding to any water body type and the remote sensing reflectance of the water body pixel of the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water body type to which the water body pixel of the hyperspectral image belongs is this any water body type; When the spectral angle cosine between the remote sensing reflectance of the water body pixel of the hyperspectral image and the average remote sensing reflectance spectra corresponding to each water body type in the preset band is less than the preset value, respectively calculate the weight value of the water body pixel of the hyperspectral image relative to each water body type, Use the following third calculation formula to calculate the first weight value of the water body pixel of the hyperspectral image relative to the oligotrophic water body type: The second weight value of the water pixel in the hyperspectral image relative to the mesotrophic water type is calculated using the following fourth calculation formula: The second weight value of the water pixel in the hyperspectral image relative to the eutrophic water type is calculated using the following fifth calculation formula: Wherein, SA1 represents the spectral angle cosine of the remote sensing reflectance of the water pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water type in the preset band, SA2 represents the spectral angle cosine of the remote sensing reflectance of the water pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water type in the preset band, and SA3 represents the spectral angle cosine of the remote sensing reflectance of the water pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water type in the preset band; A third determination module, configured to determine the target chlorophyll a concentration corresponding to the water pixel in the hyperspectral image by using a chlorophyll a inversion model corresponding to the target water type.

6. An apparatus for retrieving the concentration of chlorophyll a in water bodies of a hyperspectral satellite, characterized in that, The device includes: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to: Obtain the remote sensing reflectance data of the sampling points in the lake reservoir water body background dataset, and calculate the equivalent remote sensing reflectance of the sampling points in the preset band of the hyperspectral satellite data. The equivalent remote sensing reflectance of the sampling points in the hyperspectral satellite data band is calculated using the following first calculation formula: Among them, represents the equivalent remote sensing reflectance of the water body in the preset band of the hyperspectral satellite data, represents the remote sensing reflectance data of the water body, f i f(λ) represents the spectral response function of the preset band of the i-th hyperspectral satellite data, λ represents the wavelength, λ min and λ max respectively represent the minimum wavelength and the maximum wavelength of the f i (λ) function; According to the equivalent remote sensing reflectance of the water body, respectively determine the average remote sensing reflectance spectra of different water types in each preset band. The average remote sensing reflectance spectra of different water types in each preset band are calculated using the following second calculation formula: Among them, represents the average remote sensing reflectance of any water body type in the preset band of the i-th hyperspectral satellite data, and N is the number of samples in the lake and reservoir water body background dataset of this water body type. Among them, the water body types include: oligotrophic water body type, mesotrophic water body type, and eutrophic water body type; Determine the chlorophyll a inversion model corresponding to each water type; Calculate the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to each water type and the remote sensing reflectance of the water pixel in the hyperspectral image in the preset band respectively, so as to determine the target water type to which the water pixel in the hyperspectral image belongs. Determining the target water type to which the water pixel in the hyperspectral image belongs includes: When the spectral angle cosine of the average remote sensing reflectance spectrum corresponding to any water type and the remote sensing reflectance of the water pixel in the hyperspectral image in the preset band is greater than or equal to the preset value, determine that the target water type to which the water pixel in the hyperspectral image belongs is this any water type; When the spectral angle cosine of the remote sensing reflectance of the water pixel in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to each water type in the preset band is less than the preset value, calculate the weight value of the water pixel in the hyperspectral image relative to each water type respectively, The first weight value of the water pixel in the hyperspectral image relative to the oligotrophic water type is calculated using the following third calculation formula: The second weight value of the water pixel in the hyperspectral image relative to the mesotrophic water type is calculated using the following fourth calculation formula: The second weight value of the water pixel in the hyperspectral image relative to the eutrophic water type is calculated using the following fifth calculation formula: Among them, SA1 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the oligotrophic water type in the preset band, SA2 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the mesotrophic water type in the preset band, and SA3 represents the spectral cosine of the remote sensing reflectance of the water pixels in the hyperspectral image and the average remote sensing reflectance spectrum corresponding to the eutrophic water type in the preset band; Using the chlorophyll a inversion model corresponding to the target water type, determine the target chlorophyll a concentration corresponding to the water pixels in the hyperspectral image.

7. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the instruction is executed by the processor, it implements the steps of the method according to any one of claims 1-4.

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