CN117972304B - Remote sensing estimation method and system for total amount of phytoplankton group algae - Google Patents
Remote sensing estimation method and system for total amount of phytoplankton group algae Download PDFInfo
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Abstract
The invention relates to the field of ocean satellite remote sensing technology and application thereof, and provides a method and a system for remotely sensing and estimating total amount of phytoplankton group algae. The method comprises the steps of obtaining total Chl-a concentration of pixels, PGs Chl-a concentration of pixels, sea surface temperature and sea surface salinity; dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity; based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established; establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity; and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
Description
Technical Field
The invention relates to the field of ocean satellite remote sensing technology and application thereof, in particular to a method and a system for remotely sensing and estimating total amount of phytoplankton group algae.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The total amount of algae in the phytoplankton group (Phytoplankton group, PGs) reflects the level of primary productivity of the ocean and the ability to regulate the climate, playing an important role in the sea food network and the bio-geochemical cycle. The continental shelf is the region with the strongest action on sea and land, and has great social and economic significance. The phytoplankton content is rich, and contributes to 15% of global net primary productivity and 40% of global carbon sequestration. Therefore, the monitoring of the total amount of the PGs algae on the land frame has important research value and scientific significance. The traditional monitoring method is to perform field investigation, has the defects of long period, low speed and the like, and the satellite remote sensing technology has higher space-time resolution and wider observation range, so that the marine surface algae content is effectively monitored. PGs, however, often exhibit vertical maldistribution due to different morphological and physiological characteristics. Therefore, to more accurately monitor the primary productivity of the continental shelf and the bio-geochemical cycling process, there is a need to monitor the total amount of PGs algae within the water column.
At present, three-dimensional remote sensing of total PGs algae in a continental shelf is almost blank, but there are many reports on remote sensing of total algae in a marine water body, and the conventional research thought is to directly establish an empirical relationship between total algae and total Chl-a concentration in a surface layer or use total Chl-a vertical Gaussian distribution, but the method is not suitable for continental shelf water bodies with high dynamic total Chl-a vertical distribution. In fact, the total Chl-a vertical structure type of the continental shelf has not only gaussian distribution but also uniform mixing and attenuation. Thus, the existing methods have certain limitations. The PGs Chl-a vertical structure type cannot be determined, a clear remote sensing judging method is not available, and the total amount of the PGs algae on the land frame cannot be estimated remotely.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a remote sensing estimation method and a remote sensing estimation system for total amount of floating plant group algae.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The first aspect of the invention provides a remote sensing estimation method for total amount of phytoplankton group algae.
A remote sensing estimation method for total amount of phytoplankton group algae comprises the following steps:
acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
Dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
Based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
Establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
Further, the vertical structural model includes a gaussian distribution function:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, a1 determines the size of PGs Chl-a, b1 represents water depth at maximum of PGs Chl-a, and c1 represents peak width of Gaussian distribution.
Further, the vertical structure model further comprises a mixing uniformity model:
；
Wherein PGs Chl-a (z) represents PGs Chl-a of water depth at z, Represents the surface layer PGs Chl-a.
Further, the vertical structure model further comprises an exponential decay model:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, m1 determines the size of PGs Chl-a, and n1 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
Further, the vertical structure model further includes a power function attenuation model:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, m2 determines the size of PGs Chl-a, and n2 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
Further, the process for dividing the vertical structure types of the concentration of brown algae, blue algae, green algae and hidden algae Chl-a by combining the fresh water, the black tide and the mixed water body in the Yangtze river comprises the following steps:
under the water diluted in the Yangtze river, brown algae are in three vertical distribution types, including Gaussian distribution, exponential decay and uniform mixing; blue algae exhibit two vertical distribution types, including gaussian distribution and exponential decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae presents three vertical distribution types, including Gaussian distribution, uniform mixing and power function attenuation;
Under the black tide, brown algae presents a vertical distribution type, namely Gaussian distribution; blue algae exhibit four vertical distribution types, including gaussian distribution, uniform mixing, exponential decay and power function decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae presents two vertical distribution types, including Gaussian distribution and power function attenuation;
Under the mixed water body, the brown algae presents two vertical distribution types, including Gaussian distribution and even mixing; blue algae exhibit three vertical distribution types, including gaussian distribution, uniform mixing and exponential decay; the green algae are in two vertical distribution types, including Gaussian distribution and uniform mixing; the hidden algae are in two vertical distribution types, including Gaussian distribution and uniform mixing.
Further, the process for establishing the vertical structural model based on the pixel total Chl-a concentration and the pixel PGs Chl-a concentration by combining the fresh water, the black tide and the mixed water body of the Yangtze river to divide the vertical structural types of the brown algae, the blue algae, the green algae and the hidden algae Chl-a concentration further comprises the following steps:
For brown algae Chl-a diluted in the Yangtze river, when Chl-a is equal to Chl-a < t1, the brown algae Chl-a decays exponentially, otherwise, the brown algae Chl-a is distributed in Gaussian; for blue algae Chl-a diluted in the Yangtze river, when the blue algae Chl-a is more than t4, the blue algae Chl-a decays exponentially, otherwise, the blue algae Chl-a is in Gaussian distribution; for green algae Chl-a under the water diluted by Yangtze river, the distribution is Gaussian; for the hidden algae Chl-a under the water diluted by the Yangtze river, when the hidden algae Chl-a is smaller than t15, the hidden algae Chl-a is uniformly mixed, when the hidden algae Chl-a is larger than t16, the hidden algae Chl-a is attenuated in a power function, and other conditions are Gaussian distribution;
for brown algae Chl-a under black tide, the distribution is Gaussian; for blue algae Chl-a under black tide, when T < T5& blue algae Chl-a is Chl-a > T6 or T > T7, the blue algae Chl-a is attenuated in a power function, when the blue algae Chl-a is Chl-a < T8, the blue algae Chl-a is uniformly mixed, when T > T9& blue algae Chl-a is Chl-a > T10, the blue algae Chl-a is exponentially attenuated, and other conditions are Gaussian distribution; for green algae Chl-a under black tide, the distribution is Gaussian; for the hidden algae Chl-a under the black tide, when the hidden algae Chl-a is more than t17, the hidden algae Chl-a decays in a power function, otherwise, the hidden algae Chl-a is in Gaussian distribution;
For brown algae Chl-a in the mixed water body, when T > T2& S > T3, the brown algae Chl-a is uniformly mixed, otherwise, the brown algae Chl-a is in Gaussian distribution; for blue algae Chl-a in the mixed water body, when T > T11 or blue algae Chl-a > T12, the blue algae Chl-a is exponentially attenuated, and when S > T13, the blue algae Chl-a is uniformly mixed, and other conditions are Gaussian distribution; for green algae under the mixed water body, when T is smaller than T14, the green algae Chl-a is in Gaussian distribution, otherwise, the green algae Chl-a is uniformly mixed; for the hidden algae Chl-a under the mixed water body, when T > T18, the hidden algae Chl-a is uniformly mixed, otherwise, the hidden algae Chl-a is distributed in Gaussian.
In a second aspect, the invention provides a remote sensing estimation system for total amount of phytoplankton group algae.
A remote sensing estimation system for total amount of phytoplankton group algae, comprising:
a data acquisition module configured to: acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
a water body partitioning module configured to: dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
A vertical structure partitioning module configured to: based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
A function creation module configured to: establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
A computing module configured to: and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
A third aspect of the present invention provides a computer-readable storage medium.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for remote sensing estimation of total amount of phytoplankton algae as described in the first aspect above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the method for remote sensing estimation of total phytoplankton according to the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
The PGs Chl-a vertical structure type is divided into four types of Gaussian distribution, uniform mixing, exponential decay and power function decay, and a corresponding function model is established. And (3) judging the vertical structural type of the PGs Chl-a by using the total Chl-a, the PGs Chl-a, T and S, reasonably parameterizing partial parameters of Gaussian distribution, exponential decay and power function decay based on the surface total Chl-a of actual measurement and remote sensing inversion, and finally integrating a parameterized vertical structural model in a corresponding water column of the pixel to obtain the total amount of PGs algae in the pixel, deducing two-dimensional surface remote sensing information into a three-dimensional space, and improving the total amount precision of the PGs algae in the pixel scale. According to the invention, the PGs Chl-a vertical distribution in the continental shelf water column is divided into four types, a PGs Chl-a parameterized vertical structure model in the pixel water column and a PGs Chl-a vertical distribution remote sensing discrimination method are constructed, and the remote sensing estimation of the total amount of PGs algae in the pixel water column is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of remote sensing discrimination of PGs Chl-a vertical distribution type shown in the invention;
FIG. 2 is a graph showing the relation between the parameters of the Gaussian function model and the measured surface PGs Chl-a in the case of vertical Gaussian distribution of PGs Chl-a; fig. 2 (a) is a graph of a parameter a1 in a brown algae gaussian distribution function and a surface brown algae Chl-a, fig. 2 (b) is a graph of a parameter a1 in a blue algae gaussian distribution function and a surface blue algae Chl-a, fig. 2 (c) is a graph of a1 in a green algae gaussian distribution function and a surface green algae Chl-a, fig. 2 (d) is a graph of a parameter a1 in a hidden algae gaussian distribution function and a surface hidden algae Chl-a, fig. 2 (e) is a graph of a parameter b1 in a brown algae gaussian distribution function and a surface brown algae Chl-a, fig. 2 (f) is a graph of a parameter b1 in a blue algae gaussian distribution function and a surface blue algae Chl-a, fig. 2 (g) is a graph of a parameter b1 in a green algae gaussian distribution function and a surface green algae Chl-a, and fig. 2 (h) is a graph of a hidden algae Chl-a;
FIG. 3 is a diagram showing the effect of the remote sensing inversion method using surface PGs Chl-a according to the present invention; fig. 3 (a) is a graph showing the concentration of surface brown algae and the concentration of surface Chl-a, fig. 3 (b) is a graph showing the concentration of surface blue algae and the concentration of surface Chl-a, fig. 3 (c) is a graph showing the primary function of the concentration of surface green algae and the concentration of surface Chl-a, and fig. 3 (d) is a graph showing the primary function of the concentration of surface estimated hidden algae and the concentration of surface hidden algae Chl-a;
FIG. 4 is a graph showing the results of verification of telemetry skin PGs Chl-a using measured data in accordance with the present invention; FIG. 4 (a) is a graph of estimating the relationship between the model and the actual measured brown algae Chl-a, FIG. 4 (b) is a graph of estimating the relationship between the model and the actual measured brown algae Chl-a, FIG. 4 (c) is a graph of estimating the relationship between the model and the actual measured green algae Chl-a, and FIG. 4 (d) is a graph of estimating the relationship between the model and the actual measured hidden algae Chl-a.
FIG. 5 is a graph showing the results of verification of estimated remote sensing PGs algae total amount using measured data according to the present invention; fig. 5 (a) is a graph of the relationship between the estimated blue algae Chl-a and the measured blue algae Chl-a obtained from the measured data, fig. 5 (b) is a graph of the relationship between the estimated green algae Chl-a and the measured green algae Chl-a obtained from the measured data, fig. 5 (c) is a graph of the relationship between the estimated hidden algae Chl-a and the measured hidden algae Chl-a obtained from the measured data, fig. 5 (d) is a graph of the relationship between the estimated MODIS-Aqua blue algae Chl-a and the measured blue algae Chl-a, fig. 5 (e) is a graph of the relationship between the estimated MODIS-Aqua green algae Chl-a and the measured green algae Chl-a, and fig. 5 (f) is a graph of the relationship between the estimated MODIS-Aqua hidden algae Chl-a and the measured hidden algae Chl-a.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Example 1
The embodiment provides a remote sensing estimation method for total amount of phytoplankton, and the method is applied to a server for illustration, and it can be understood that the method can also be applied to a terminal, a server and a system, and can be realized through interaction of the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein. In this embodiment, the method includes the steps of:
acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
Dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
Based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
Establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
In order to calculate the total amount of PGs algae in the remote sensing pixel, namely the total amount of PGs algae in the corresponding water column of the pixel, the invention constructs a vertical distribution function of the PGs of the parameterized continental shelf and combines the acquired multi-source satellite remote sensing data. The total amount of algae is characterized by chlorophyll a (Chl-a) content. The specific scheme is as follows:
Based on the actually measured biological-optical-environmental data, dividing the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration, including Gaussian distribution, uniform mixing, exponential decay and power function decay, and establishing a vertical structure model and parameterization;
Establishing a vertical structure type discrimination function according to the measured total Chl-a concentration, PGs Chl-a concentration, sea surface temperature (T) and sea surface salinity (S); the vertical structure type discrimination function is to discriminate the vertical structure type of each PG by using the parameters acquired by satellite remote sensing, and the specific method is shown in figure 1, for example, the discrimination function used is as follows: brown algae Chl-a at CDW (T >23 ℃ and S < 30): chl-a <0.2.
The parameterized vertical structure model and the vertical structure type discriminant function are applied to the satellite remote sensing pixel, the integral is obtained on the water depth above the true light layer in the pixel, and the total amount of PGs algae in the pixel is calculated by combining the pixel area, wherein the calculation formula is as follows:
。（1）
wherein S represents the water surface area covered by the pixel, namely the resolution of the satellite remote sensing image; the value of i is 1-4, and the i represents brown algae, blue algae, green algae and hidden algae respectively; surface layer Chl-a representing the ith PG; /(I) A Chl-a vertical structure parameterized model representing the ith PG; z represents from the surface layer to/>The water depths of all layers are the same.
The invention establishes 4 vertical structural models which are all obtained by the best fitting of measured data, and are shown in formulas (2) - (5):
① Gaussian distribution function:
。（2）
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, a1 determines the size of PGs Chl-a, b1 represents water depth at maximum of PGs Chl-a, and c1 represents peak width of Gaussian distribution.
② Mixing evenly model:
。（3）
In the method, in the process of the invention, Represents the surface layer PGs Chl-a.
③ Exponential decay model:
。（4）
Where m1 determines the size of PGs Chl-a and n1 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
④ Power function decay model:
。（5）
Where m2 determines the size of PGs Chl-a and n2 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
In some embodiments, for the uniformly mixed model, the PGs Chl-a concentration of any depth in the water column is represented by the PGs Chl-a concentration of the surface layer;
in some embodiments, for Gaussian distribution, exponential decay and power function decay models, a relation between the measured total Chl-a concentration of the surface layer and model parameters a1, b1, c1, m1, n1, m2 and n2 is established, model parameterization is achieved, and then each relation is applied to the total Chl-a concentration of the surface layer of the remote sensing pixel.
The judging parameters of the vertical structure type of the remote sensing pixel are as follows: total Chl-a concentration of pels, chl-a concentration of pels PGs, temperature (T) and salinity (S).
The study area water is divided into three water masses based on temperature and salinity, namely Yangtze river fresh water (CDW) (T >23 ℃ and S < 30%o), black tide (KW) (T >23 ℃ and S > = 32.9%o) and Mixed Water (MW) (others).
According to the measured data, under CDW, brown algae presents three vertical distribution types including Gaussian distribution, exponential decay and uniform mixing; blue algae exhibit two vertical distribution types, including gaussian distribution and exponential decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae presents three vertical distribution types, including gaussian distribution, uniform mixing and power function attenuation. Under KW, brown algae presents a vertical distribution type, namely Gaussian distribution; blue algae exhibit four vertical distribution types, including gaussian distribution, uniform mixing, exponential decay and power function decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae exhibit two types of vertical distributions, including gaussian distributions and power function decays. Under MW, brown algae presents two vertical distribution types, including Gaussian distribution and even mixing; blue algae exhibit three vertical distribution types, including gaussian distribution, uniform mixing and exponential decay; the green algae are in two vertical distribution types, including Gaussian distribution and uniform mixing; the hidden algae are in two vertical distribution types, including Gaussian distribution and uniform mixing.
For brown algae Chl-a under CDW, when Chl-a is equal to Chl-a < t1, the brown algae Chl-a decays exponentially, otherwise, the brown algae Chl-a is distributed in Gaussian; for blue algae Chl-a under CDW, when blue algae Chl-a > t4, blue algae Chl-a decays exponentially, otherwise, the blue algae Chl-a is in Gaussian distribution; for green algae Chl-a under CDW, the distribution is Gaussian; for the hidden algae Chl-a under CDW, when the hidden algae Chl-a is smaller than t15, the hidden algae Chl-a is uniformly mixed, when the hidden algae Chl-a is larger than t16, the hidden algae Chl-a is attenuated in a power function, and other conditions are Gaussian distribution. For brown algae Chl-a under KW, the distribution is Gaussian; for blue algae Chl-a under KW, when T < T5& blue algae Chl-a is Chl-a > T6 or T > T7, the blue algae Chl-a is attenuated in a power function, when blue algae Chl-a is Chl-a < T8, the blue algae Chl-a is uniformly mixed, when T > T9& blue algae Chl-a is Chl-a > T10, the blue algae Chl-a is attenuated in an exponential manner, and other conditions are Gaussian distribution; for green algae Chl-a under KW, the distribution is Gaussian; for the hidden algae Chl-a under KW, when the hidden algae Chl-a is greater than t17, the hidden algae Chl-a decays in a power function, otherwise, the hidden algae Chl-a is in Gaussian distribution. For brown algae Chl-a under MW, when T > T2& S > T3, the brown algae Chl-a is uniformly mixed, otherwise, the brown algae Chl-a is in Gaussian distribution; for blue algae Chl-a under MW, when T > T11 or blue algae Chl-a > T12, blue algae Chl-a decays exponentially, and when S > T13, blue algae Chl-a is uniformly mixed, otherwise, the blue algae Chl-a is in Gaussian distribution; for green algae under MW, when T is less than T14, the green algae Chl-a is in Gaussian distribution, otherwise, the green algae Chl-a is uniformly mixed; for the hidden algae Chl-a under MW, when T > T18, the hidden algae Chl-a is uniformly mixed, otherwise, the hidden algae Chl-a is distributed in Gaussian.
Wherein T1-T18 are thresholds, and each two groups of data are divided according to the thresholds, and the T test results are obviously uncorrelated.
Wherein the depth of water in the water column is derived from the remote sensing reflectivity (Rrs).
More specifically, the remote sensing image data is MODIS-Aquadata.
The invention selects the China continental shelf sea-east sea as a typical research area, and based on actual measurement biological-optical-environment data and MODIS-Aqua satellite remote sensing data, the invention is specifically described to realize the remote sensing estimation of the total amount of the PGs algae on the continental shelf by applying the method of the invention. The detailed description and the results are further described below with reference to the accompanying drawings:
(1) Remote sensing judgment is carried out on the vertical distribution type of PGs Chl-a in the pixel;
The vertical distribution type of the pixel PGs Chl-a is determined based on the sea water remote sensing surface T, S, the total Chl-a and the PGs Chl-a, and the classification index and the result are shown in figure 1. The values of t1 to t18 are shown in Table 1.
TABLE 1 thresholds in PGs Chl-a vertical structure type remote sensing discrimination index in pixels
Specifically, the MODIS-Aqua total Chl-a and T data are directly applied, S data are derived from MODIS-Aqua Rrs, the data obtained by remote sensing inversion are verified by using measured data, and the result shows that the remote sensing inversion total Chl-a, T and S have good accuracy. For remote sensing estimation of the surface layer PGs Chl-a, an empirical PGs Chl-a algorithm is established according to the measured data, as shown in FIG. 3, wherein (a) in FIG. 3 is a graph between the surface layer brown algae and the surface layer Chl-a concentration, (b) in FIG. 3 is a graph between the surface layer blue algae and the surface layer Chl-a concentration, and (c) in FIG. 3 is a primary function graph between the surface layer green algae and the surface layer Chl-a concentration, and (d) in FIG. 3 is a primary function graph between the surface layer estimation hidden algae and the surface layer hidden algae Chl-a concentration. Furthermore, the accuracy of the result of the verification of the estimated remote sensing surface layer PGs Chl-a according to the measured data is better, and the accuracy of the result of the estimated remote sensing surface layer PGs Chl-a is determined by R2 (determination coefficient) and R (correlation coefficient). R 2 is 0-1, and R is-1; the closer R 2 is to 1, the better the fitting result of the regression line to the data is shown; r <0 indicates a negative correlation between data, and r >0 indicates a positive correlation between data, and the closer the absolute value of r is to 1, the stronger the correlation between data.
As shown in fig. 4, fig. 4 (a) is a graph of estimating MODIS-Aqua brown algae Chl-a and actually measured brown algae Chl-a, fig. 4 (b) is a graph of estimating MODIS-Aqua blue algae Chl-a and actually measured blue algae Chl-a, fig. 4 (c) is a graph of estimating MODIS-Aqua green algae Chl-a and actually measured green algae Chl-a, and fig. 4 (d) is a graph of estimating MODIS-Aqua hidden algae Chl-a and actually measured hidden algae Chl-a; the data in each scatter plot are mostly distributed on the fitting regression line, and the data dispersion is small; the closer the discreteness is to 0, the better the resulting accuracy. For the fitted regression line, the form is y=ax+b, a represents the gradient, the value range is-1 to 1, when a <0, it indicates that the data are negatively correlated, it indicates that y is underestimated, otherwise, it is positively correlated, it indicates that y is overestimated, and a is best when a is close to 1. FIG. 4 is a class of data validation results, which can be generalized in terms of R2, R, discretization (N), and grade (p).
(2) PGs Chl-a vertical structure type construction and parameterization;
The invention constructs four vertical structural models which are respectively Gaussian distribution function, uniform mixing function, exponential decay function and power decay function and are respectively represented by formulas (1) - (4). Parameterization is required for gaussian distribution functions, exponential decay functions and power decay functions, i.e. estimations of a1, b1, c1, m1, n1, m2 and n 2. The invention finds that the parameters (a 1 and b 1) of the Gaussian distribution function have strong correlation with the measured surface layer PGs Chl-a, and the variation range of c1 is smaller, so that the average value is used. As shown in fig. 2, (a) in fig. 2 is a graph of parameters a1 and surface-layer brown algae Chl-a in a brown algae gaussian distribution function, (b) in fig. 2 is a graph of parameters a1 and surface-layer blue algae Chl-a in a blue algae gaussian distribution function, (c) in fig. 2 is a graph of parameters a1 and surface-layer green algae Chl-a in a green algae gaussian distribution function, (d) in fig. 2 is a graph of parameters a1 and surface-layer hidden algae Chl-a in a hidden algae gaussian distribution function, (e) in fig. 2 is a graph of parameters b1 and surface-layer brown algae Chl-a in a brown algae gaussian distribution function, (f) in fig. 2 is a graph of parameters b1 and surface-layer blue algae Chl-a in a blue algae gaussian distribution function, and (g) in fig. 2 is a graph of parameters b1 and surface-layer green algae Chl-a in a hidden algae gaussian distribution function. Specifically, the values of the parameter c1 in the brown algae Chl-a, blue algae Chl-a and green algae Chl-a gaussian distribution functions are 10.62, 11.85 and 9.74, respectively, while the values of the parameter c1 in the hidden algae Chl-a gaussian distribution function are divided into two cases, one is c1= 1273.20 × (hidden algae Chl-a concentration) +0.32 when hidden algae Chl-a <0.025 mg m-3, and the other is c1=6.39.
(3) And integrating the water depth of the euoptical layer in each pixel according to the PGs Chl-a vertical distribution function to obtain the total amount of PGs algae in the corresponding water column of each pixel. The MODIS-Aqua remote sensing data is adopted, and the pixel area is 1 km multiplied by 1 km.
Finally, the method is applied to the actual measurement data to obtain the estimated total amount of PGs algae, and the actual measurement data is used for verification. Because of the limited number of samples, the vertical distribution types of PGs Chl-a are Gaussian distribution, and the verification result shows that the estimated algae total amounts of blue algae, green algae and hidden algae are in a significant correlation with the measured data, as shown in fig. 5. More specifically, the estimated total amount of remote sensing PGs algae is verified according to the measured data (fig. 5), and the estimated total amount of remote sensing algae of blue algae, green algae and hidden algae is in a significant correlation with the measured data, but the error is larger than the estimated total amount of PGs algae based on the measured data, which indicates that the accuracy of the remote sensing data needs to be improved. As shown in fig. 5, (a) in fig. 5 is a graph of estimated blue algae Chl-a and measured blue algae Chl-a obtained from measured data, (b) in fig. 5 is a graph of estimated green algae Chl-a and measured green algae Chl-a obtained from measured data, (c) in fig. 5 is a graph of estimated hidden algae Chl-a and measured hidden algae Chl-a obtained from measured data, (d) in fig. 5 is a graph of estimated MODIS-Aqua blue algae Chl-a and measured blue algae Chl-a, and (e) in fig. 5 is a graph of estimated MODIS-Aqua green algae Chl-a and measured green algae Chl-a, and (f) in fig. 5 is a graph of estimated MODIS-Aqua hidden algae Chl-a and measured hidden algae Chl-a. The accuracy of the estimated data results is illustrated from R2, R, the discreteness (N) and the gradient (p), and in fig. 5, the discreteness of the data is relatively large, especially the estimated data of remote sensing (second row), while the estimated data based on the measured data (first row) is relatively small, which means that the error source with large discreteness of the estimated data of remote sensing is satellite remote sensing data, so that the accuracy of the satellite remote sensing data needs to be improved.
Example two
The embodiment provides a remote sensing estimation system for total amount of phytoplankton group algae.
A remote sensing estimation system for total amount of phytoplankton group algae, comprising:
a data acquisition module configured to: acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
a water body partitioning module configured to: dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
A vertical structure partitioning module configured to: based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
A function creation module configured to: establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
A computing module configured to: and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
It should be noted that, the data acquisition module, the water body division module, the vertical structure division module, the function building module and the calculation module are the same as the examples and application scenarios implemented by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the remote sensing estimation method for total amount of phytoplankton algae according to the above embodiment.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the steps in the remote sensing estimation method for total amount of phytoplankton according to the first embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A remote sensing estimation method for total amount of phytoplankton group algae is characterized by comprising the following steps:
acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
Dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
Based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
the concrete process for establishing the vertical structure model comprises the following steps:
For brown algae Chl-a diluted in the Yangtze river, when Chl-a is equal to Chl-a < t1, the brown algae Chl-a decays exponentially, otherwise, the brown algae Chl-a is distributed in Gaussian; for blue algae Chl-a diluted in the Yangtze river, when the blue algae Chl-a is more than t4, the blue algae Chl-a decays exponentially, otherwise, the blue algae Chl-a is in Gaussian distribution; for green algae Chl-a under the water diluted by Yangtze river, the distribution is Gaussian; for the hidden algae Chl-a under the water diluted by the Yangtze river, when the hidden algae Chl-a is smaller than t15, the hidden algae Chl-a is uniformly mixed, when the hidden algae Chl-a is larger than t16, the hidden algae Chl-a is attenuated in a power function, and other conditions are Gaussian distribution;
For brown algae Chl-a under black tide, the distribution is Gaussian; for blue algae Chl-a under black tide, when T < T5 & blue algae Chl-a is Chl-a > T6 or T > T7, the blue algae Chl-a is attenuated in a power function, when the blue algae Chl-a is Chl-a < T8, the blue algae Chl-a is uniformly mixed, when T > T9 & blue algae Chl-a is Chl-a > T10, the blue algae Chl-a is exponentially attenuated, and other conditions are Gaussian distribution; for green algae Chl-a under black tide, the distribution is Gaussian; for the hidden algae Chl-a under the black tide, when the hidden algae Chl-a is more than t17, the hidden algae Chl-a decays in a power function, otherwise, the hidden algae Chl-a is in Gaussian distribution;
For brown algae Chl-a in the mixed water body, when T > T2 & S > T3, the brown algae Chl-a is uniformly mixed, otherwise, the brown algae Chl-a is in Gaussian distribution; for blue algae Chl-a in the mixed water body, when T > T11 or blue algae Chl-a > T12, the blue algae Chl-a is exponentially attenuated, and when S > T13, the blue algae Chl-a is uniformly mixed, and other conditions are Gaussian distribution; for green algae under the mixed water body, when T is smaller than T14, the green algae Chl-a is in Gaussian distribution, otherwise, the green algae Chl-a is uniformly mixed; for the hidden algae Chl-a under the mixed water body, when T is more than T18, the hidden algae Chl-a is uniformly mixed, otherwise, the hidden algae Chl-a is distributed in Gaussian;
Establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
2. The method for remotely sensing and estimating total amount of phytoplankton according to claim 1, wherein the vertical structural model comprises a gaussian distribution function:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, a1 determines the size of PGs Chl-a, b1 represents water depth at maximum of PGs Chl-a, and c1 represents peak width of Gaussian distribution.
3. The method for remotely sensing and estimating total amount of phytoplankton according to claim 1, wherein the vertical structural model further comprises a uniformly mixed model:
；
Wherein PGs Chl-a (z) represents PGs Chl-a of water depth at z, Represents the surface layer PGs Chl-a.
4. The method for remotely sensing and estimating total amount of phytoplankton according to claim 1, wherein the vertical structural model further comprises an exponential decay model:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, m1 determines the size of PGs Chl-a, and n1 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
5. The method for remotely sensing and estimating total amount of phytoplankton according to claim 1, wherein the vertical structural model further comprises a power function attenuation model:
；
Where PGs Chl-a (z) represents PGs Chl-a of water depth at z, m2 determines the size of PGs Chl-a, and n2 represents the attenuation coefficient of PGs Chl-a with increasing water depth.
6. The method for remotely sensing and estimating total amount of phytoplankton algae according to claim 1, wherein the process of dividing the vertical structure type of the concentration of brown algae, blue algae, green algae and hidden algae Chl-a by combining the fresh water, the black tide and the mixed water body of Yangtze river comprises the following steps:
under the water diluted in the Yangtze river, brown algae are in three vertical distribution types, including Gaussian distribution, exponential decay and uniform mixing; blue algae exhibit two vertical distribution types, including gaussian distribution and exponential decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae presents three vertical distribution types, including Gaussian distribution, uniform mixing and power function attenuation;
Under the black tide, brown algae presents a vertical distribution type, namely Gaussian distribution; blue algae exhibit four vertical distribution types, including gaussian distribution, uniform mixing, exponential decay and power function decay; green algae exhibit a vertical distribution type, i.e., gaussian distribution; the hidden algae presents two vertical distribution types, including Gaussian distribution and power function attenuation;
Under the mixed water body, the brown algae presents two vertical distribution types, including Gaussian distribution and even mixing; blue algae exhibit three vertical distribution types, including gaussian distribution, uniform mixing and exponential decay; the green algae are in two vertical distribution types, including Gaussian distribution and uniform mixing; the hidden algae are in two vertical distribution types, including Gaussian distribution and uniform mixing.
7. A remote sensing estimation system for total amount of phytoplankton, comprising:
a data acquisition module configured to: acquiring the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
a water body partitioning module configured to: dividing the water body into Yangtze river fresh water, black tide and mixed water body based on sea surface temperature and sea surface salinity;
A vertical structure partitioning module configured to: based on the total Chl-a concentration of the pixels and the PGs Chl-a concentration of the pixels, the vertical structure types of brown algae, blue algae, green algae and hidden algae Chl-a concentration are divided by combining the fresh water, the black tide and the mixed water body of the Yangtze river, and a vertical structure model is established;
the concrete process for establishing the vertical structure model comprises the following steps:
For brown algae Chl-a diluted in the Yangtze river, when Chl-a is equal to Chl-a < t1, the brown algae Chl-a decays exponentially, otherwise, the brown algae Chl-a is distributed in Gaussian; for blue algae Chl-a diluted in the Yangtze river, when the blue algae Chl-a is more than t4, the blue algae Chl-a decays exponentially, otherwise, the blue algae Chl-a is in Gaussian distribution; for green algae Chl-a under the water diluted by Yangtze river, the distribution is Gaussian; for the hidden algae Chl-a under the water diluted by the Yangtze river, when the hidden algae Chl-a is smaller than t15, the hidden algae Chl-a is uniformly mixed, when the hidden algae Chl-a is larger than t16, the hidden algae Chl-a is attenuated in a power function, and other conditions are Gaussian distribution;
For brown algae Chl-a under black tide, the distribution is Gaussian; for blue algae Chl-a under black tide, when T < T5 & blue algae Chl-a is Chl-a > T6 or T > T7, the blue algae Chl-a is attenuated in a power function, when the blue algae Chl-a is Chl-a < T8, the blue algae Chl-a is uniformly mixed, when T > T9 & blue algae Chl-a is Chl-a > T10, the blue algae Chl-a is exponentially attenuated, and other conditions are Gaussian distribution; for green algae Chl-a under black tide, the distribution is Gaussian; for the hidden algae Chl-a under the black tide, when the hidden algae Chl-a is more than t17, the hidden algae Chl-a decays in a power function, otherwise, the hidden algae Chl-a is in Gaussian distribution;
For brown algae Chl-a in the mixed water body, when T > T2 & S > T3, the brown algae Chl-a is uniformly mixed, otherwise, the brown algae Chl-a is in Gaussian distribution; for blue algae Chl-a in the mixed water body, when T > T11 or blue algae Chl-a > T12, the blue algae Chl-a is exponentially attenuated, and when S > T13, the blue algae Chl-a is uniformly mixed, and other conditions are Gaussian distribution; for green algae under the mixed water body, when T is smaller than T14, the green algae Chl-a is in Gaussian distribution, otherwise, the green algae Chl-a is uniformly mixed; for the hidden algae Chl-a under the mixed water body, when T is more than T18, the hidden algae Chl-a is uniformly mixed, otherwise, the hidden algae Chl-a is distributed in Gaussian;
A function creation module configured to: establishing a vertical structure type discrimination function according to the total Chl-a concentration of pixels, the PGs Chl-a concentration of pixels, the sea surface temperature and the sea surface salinity;
A computing module configured to: and applying the vertical structure model and the vertical structure type discriminant function to the satellite remote sensing pixels, and calculating the total amount of PGs algae in the pixels.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method for remote sensing estimation of total amount of phytoplankton algae according to any of claims 1-6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method for remotely sensing the total amount of phytoplankton algae of any of claims 1-6.
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