CN112362544A - Particle organic carbon monitoring method and system based on hyperspectral remote sensing - Google Patents
Particle organic carbon monitoring method and system based on hyperspectral remote sensing Download PDFInfo
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Abstract
The invention provides a particle organic carbon monitoring method and system based on hyperspectral remote sensing, wherein the method comprises the following steps: measuring a water surface spectrum through a hyperspectral remote sensing sensor to obtain a water surface remote sensing reflectivity; measuring an underwater spectrum through a backscattering type sensor to obtain a backscattering coefficient of the water body; obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity and the backscattering coefficient, and further obtaining the concentration of endogenous granular organic carbon; and obtaining the ratio of the endogenous granular organic carbon to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley value of the remote sensing reflectivity at the wavelength of 674nm, further obtaining the concentration of the exogenous granular organic carbon, and realizing the monitoring of the granular organic carbon in the water body. The method can simultaneously calculate the concentrations of endogenous, exogenous and total granular organic carbon in the water body aiming at the water ecological environment of China, realize the real-time monitoring of the granular organic carbon in the water body, and has important significance for researching the influence of the water ecological environment on the surrounding environment.
Description
Technical Field
The invention relates to the technical field of monitoring, in particular to a method and a system for monitoring granular organic carbon.
Background
Particulate Organic Carbon (POC), which is an organic particulate matter insoluble in water, plays an important role in carbon cycle. POC in a body of water can be divided into two parts, life and non-life. Life POC comes from a biological production process, including microminiature photosynthetic phytoplankton, macroalgae, as well as bacteria, fungi, bacteriophages, zooplankton, small fish, shrimps, and the like; non-living POC is also known as organic debris, including debris, feces, etc. produced during biological life activities in a body of water. POC in inland water is derived from both exogenous (also called terrestrial) and endogenous sources. Wherein, the external source input is mainly carried by surface runoff and closely related to the ecological environment around the water body and human activities. Therefore, the research on the concentration of the organic carbon in the foreign particles in the water body can provide parameters for the research on carbon sources and carbon sinks in the water body, can reflect the ecological environment state of the surrounding land and provides data support for ecological environment supervision.
The traditional measurement of the concentration of organic carbon in particles is usually observed by a dichromate wet oxidation method or a high-temperature combustion method, and the source of the organic carbon is judged by analyzing by an isotope measurement method, wherein the methods need to collect a water sample firstly and then measure in a laboratory. Limited by the sampling points, the sampling observation can only obtain the observed value of discrete sampling points, and the particle organic carbon concentration of the whole water area cannot be synchronously obtained. The technology for estimating the concentration of the organic carbon in the particles by using satellite remote sensing data is developed in recent years and is mainly applied to the research of ocean carbon cycle, but the existing remote sensing technology cannot distinguish the source of the organic carbon in the particles.
Disclosure of Invention
Aiming at the problems, the invention provides a particle organic carbon monitoring method and system based on hyperspectral remote sensing, which effectively solve the technical problem that the particle organic carbon source cannot be obtained in the particle organic carbon monitoring method.
The technical scheme provided by the invention is as follows:
a particle organic carbon monitoring method based on hyperspectral remote sensing comprises the following steps:
measuring a water surface spectrum through a hyperspectral remote sensing sensor, and further obtaining the water surface remote sensing reflectivity;
measuring an underwater spectrum through a backscattering type sensor to further obtain a backscattering coefficient of the water body;
obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity and the backscattering coefficient, and further obtaining the concentration of the endogenous granular organic carbon by a regression analysis method;
obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley values of the remote sensing reflectivity at the wavelength of 674 nm;
and obtaining the concentration of the exogenous granular organic carbon according to the ratio of the endogenous granular organic carbon to the total organic carbon concentration and the endogenous granular organic carbon concentration, thereby realizing the monitoring of the granular organic carbon in the water body.
Further preferably, in the step of obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the remote sensing reflectivity and the backscattering coefficient of the water surface, further obtaining the concentration of the endogenous granular organic carbon by a regression analysis method,
the phytoplankton absorption coefficient aph (620) at 620nm wavelength is:
wherein R isrs(indicates remote reflectance at nm wavelength, a)w(x) represents the absorption coefficient of pure water at a wavelength of nm, bb(778) Representing the backscattering coefficient of the water body at the wavelength of 778 nm;
endogenous particle organic carbon concentration CendComprises the following steps:
Cend＝v1×aph(620)+v2
where v1 and v2 represent regression equation coefficients.
Further preferably, in the ratio of the concentration of the endogenous granular organic carbon in the total organic carbon obtained according to the reflection peaks of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley of the remote sensing reflectivity at the wavelength of 674nm, the ratio RendComprises the following steps:
wherein a and b represent regression coefficients.
Further preferably, in the step of obtaining the exogenous particle organic carbon concentration according to the ratio of the endogenous particle organic carbon to the total organic carbon concentration and the endogenous particle organic carbon concentration, the exogenous particle organic carbon concentration is Cter：
Cter＝Cpoc-Cend
Wherein, CpocIs the total organic carbon concentration, and
the invention also provides a particle organic carbon monitoring system based on hyperspectral remote sensing, which comprises:
the hyperspectral remote sensing sensor is used for measuring and obtaining a water surface spectrum;
the backscattering type sensor is used for measuring and obtaining an underwater spectrum;
terminal equipment of built-in granule organic carbon monitoring devices, built-in granule organic carbon monitoring devices includes:
the data acquisition module is in communication connection with the hyperspectral remote sensing sensor and the back scattering type sensor respectively and is used for acquiring the water surface spectrum and the underwater spectrum;
the water surface remote sensing reflectivity acquisition module is used for obtaining the water surface remote sensing reflectivity according to the water surface spectrum measured by the hyperspectral remote sensing sensor;
the backscattering coefficient acquisition module is used for obtaining the backscattering coefficient of the water body according to the underwater spectrum measured by the backscattering sensor;
the operation module is used for obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity obtained by the water surface remote sensing reflectivity obtaining module and the backscattering coefficient obtained by the backscattering coefficient obtaining module, and further obtaining the concentration of the organic carbon in the endogenous particles by a regression analysis method; obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley values of the remote sensing reflectivity at the wavelength of 674 nm; and obtaining the concentration of the exogenous granular organic carbon according to the ratio of the endogenous granular organic carbon to the total organic carbon concentration and the endogenous granular organic carbon concentration, thereby realizing the monitoring of the granular organic carbon in the water body.
Further preferably, in the operation module, the absorbance coefficient aph (620) of phytoplankton at the wavelength of 620nm is:
wherein R isrs(indicates remote reflectance at nm wavelength, a)w(x) represents the absorption coefficient of pure water at a wavelength of nm, bb(778) Representing the backscattering coefficient of the water body at the wavelength of 778 nm;
endogenous particle organic carbon concentration CendComprises the following steps:
Cend＝v1×aph(620)+v2
where v1 and v2 represent regression equation coefficients.
Further preferably, in the operation module, the ratio R of the concentration of the endogenous granular organic carbon to the total organic carbon isendComprises the following steps:
wherein a and b represent regression coefficients.
Further preferably, in the operation module, the organic carbon concentration of the foreign particles is Cter：
Cter＝Cpoc-Cend
Wherein, CpocIs the total organic carbon concentration, and
the invention also provides terminal equipment which comprises a memory, a processor and a computer program which is stored in the memory and can be run on the processor, wherein the steps of the particle organic carbon monitoring method based on hyperspectral remote sensing are realized when the processor runs the computer program.
The invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the particle organic carbon monitoring method based on hyperspectral remote sensing.
According to the particle organic carbon monitoring method and system based on hyperspectral remote sensing, provided by the invention, the concentrations of endogenous source, exogenous source and total particle organic carbon in the water body can be obtained by calculation aiming at the water ecological environment of China, and the method and system are particularly suitable for the water body containing suspended sediment with higher concentration. The calculation of the concentration of the organic carbon in the exogenous particles can definitely give the total amount of the organic carbon in the particles input from the external source in the lake, and the method has important significance for researching the influence of the surrounding environment on the water ecological environment.
Drawings
The foregoing features, technical features, advantages and embodiments are further described in the following detailed description of the preferred embodiments, which is to be read in connection with the accompanying drawings.
FIG. 1 is a schematic flow chart of a particle organic carbon monitoring method based on hyperspectral remote sensing in the invention;
FIG. 2 is a schematic structural diagram of a particle organic carbon monitoring device based on hyperspectral remote sensing in the invention;
FIG. 3 is a graph illustrating the organic carbon concentration of the lake Hongze particles in an example;
fig. 4 is a schematic structural diagram of a terminal device according to the present invention.
Reference numerals:
100-built-in particle organic carbon monitoring device, 110-data acquisition module, 120-water surface remote sensing reflectivity acquisition module, 130-backscattering coefficient acquisition module and 140-operation module.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
As shown in fig. 1, which is a schematic flow chart of a monitoring method for particulate organic carbon based on hyperspectral remote sensing provided by the present invention, it can be seen from the diagram that the monitoring method for particulate organic carbon comprises:
s10, measuring the water surface spectrum through a hyperspectral remote sensing sensor, and further obtaining the water surface remote sensing reflectivity;
s20, measuring an underwater spectrum through a backscattering sensor, and further obtaining a backscattering coefficient of the water body;
s30, obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity and the backscattering coefficient, and further obtaining the concentration of the endogenous granular organic carbon by a regression analysis method;
s40, obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the valley values of the remote sensing reflection peak at the wavelength of 674 nm;
s50, obtaining the concentration of the exogenous organic carbon particles according to the ratio of the endogenous organic carbon particles to the total organic carbon concentration and the endogenous organic carbon concentration, and monitoring the organic carbon particles in the water body.
Specifically, in step S10, the water surface remote sensing reflectivity is measured by using a hyperspectral remote sensing sensor, which may be a handheld field spectrum radiometer, a hyperspectral imager, or an onboard or spaceborne remote sensing sensor. In practical application, the satellite remote sensing image of the corresponding water area can be downloaded from the internet, for example, the satellite remote sensing image of the sentinel 3 OLCI sensor of the Hongze lake is downloaded from the internet, and then the corresponding OLCI data is subjected to atmospheric correction and calculated to obtain the water surface remote sensing reflectivity.
Endogenous granular organic carbon mainly comes from phytoplankton in a water body and has close correlation with the concentration of the phytoplankton, so that the phytoplankton can be used as an intermediate variable to invert the concentration. The phytoplankton absorption is a main factor causing light attenuation in the water body, and has a large influence on the remote sensing reflectivity of the water surface, so that the phytoplankton absorption coefficient can be estimated from the remote sensing reflectivity. The absorption of phycocyanin and chlorophyll in phytoplankton can cause the valley peak characteristics of the remote sensing reflectivity near 620nm (nanometer) and 709nm, and the 620nm is the absorption position of phycocyanin, so the absorption coefficient of the phytoplankton at the 620nm can be calculated according to the formula (1), and the absorption coefficient aph (620) is unique to the cyanobacteria:
wherein R isrs(ii) remote reflectance at nm wavelength, Rrs(709) Representing remote sensing reflectance, R, at 709nm wavelengthrs(620) Represents the remote sensing reflectivity at a wavelength of 620 nm; a isw(. about.) denotes the absorption coefficient of pure water at a wavelength of about nm, i.e. aw(709) Denotes the absorption coefficient of pure water at a wavelength of 709nm, aw(620) Represents the absorption coefficient of pure water at the wavelength of 620 nm; bb(778) Representing the backscattering coefficient of a water body at a wavelength of 778 nm. For the measurement of the backscattering coefficient at the 778nm wavelength, in practical application, the measurement can be observed by using instruments such as HS6 (HOBI Labs Hydroscat-6P HS-6 backscattering measurement instrument, USA), BB9 (backscattering instrument ECO-BB9, Wetlabs, USA) and the like. It should be clear that the reason why the 778nm band is used here is that the OLCI sensor apparatus is able to detect this band, and in general, for the detection of the backscatter coefficient of a body of water, a measurement for a band of wavelengths greater than 750nm is possible, and the longer the wavelength the better.
Calculated 62 toThe measured concentration C of organic carbon in the endogenous particles in the study area after the 0nm phytoplankton absorption coefficient aph (620)endThe concentration C of the organic carbon of the endogenous particles can be obtained by carrying out regression analysis on the concentration C and the absorption coefficient aph (620)endAs shown in formula (2):
Cend＝v1×aph(620)+v2 (2)
where v1 and v2 represent regression equation coefficients.
Because the ratio of organic carbon of the endogenous particles to the exogenous particles directly influences the sizes of the reflection peaks of the remote sensing reflectivities of 560nm and 709nm and the size of the reflection valley of 674nm, the depth of the envelope curve of the remote sensing reflectivities is calculated by utilizing the reflection peak values of the remote sensing reflectivities of 560nm and 709nm and the valley value of the reflection peak value of 674nm, and the regression equation is calculated by utilizing the actual measurement ratio of the endogenous particles to the exogenous particles, wherein the formula is as follows (3):
wherein R isendThe ratio of the organic carbon of the endogenous particles to the concentration of the total organic carbon is shown, and a and b represent regression coefficients. Here, the higher the endogenous ratio (the ratio of endogenous to total source, i.e., the ratio of endogenous particulate organic carbon concentration to total source), RendThe smaller the value.
In practical application, values of the regression equation coefficients v1 and v2 and the regression coefficients a and b in the formulas (2) and (3) are shown in table 1:
table 1: regression equation coefficients and regression coefficient values
The ratio R of the organic carbon of the endogenous particles to the total organic carbon concentration is obtained according to calculationendTo obtain the total organic carbon concentration CpocAs in formula (4):
then calculating according to the formula (5) to obtain the organic carbon concentration of the exogenous particles as Cter：
Cter＝Cpoc-Cend (5)
In the particle organic carbon monitoring method, the concentrations of endogenous, exogenous and total particle organic carbon in the water body are obtained by simultaneously calculating aiming at the water ecological environment of China, so that the total amount of particle organic carbon input from the external source in the lake is definitely given, the real-time monitoring of the particle organic carbon in the water body is realized, and the method has important significance for researching the influence of the surrounding environment on the water ecological environment.
The invention also provides a particle organic carbon monitoring system based on hyperspectral remote sensing, which comprises: the hyperspectral remote sensing sensor is used for measuring and obtaining a water surface spectrum; the backscattering type sensor is used for measuring and obtaining an underwater spectrum; a terminal device with a built-in granular organic carbon monitoring apparatus, a built-in granular organic carbon monitoring apparatus 100, as shown in fig. 2, includes: the data acquisition module 110 is in communication connection with the hyperspectral remote sensing sensor and the backscattering sensor respectively and is used for acquiring a water surface spectrum and an underwater spectrum; the water surface remote sensing reflectivity acquisition module 120 is used for obtaining the water surface remote sensing reflectivity according to the water surface spectrum measured by the hyperspectral remote sensing sensor; a backscattering coefficient obtaining module 130, configured to obtain a backscattering coefficient of the water body according to the underwater spectrum measured by the backscattering sensor; the operation module 140 is configured to obtain an absorption coefficient of the phytoplankton at a wavelength of 620nm according to the water surface remote sensing reflectivity obtained by the water surface remote sensing reflectivity obtaining module and the backscattering coefficient obtained by the backscattering coefficient obtaining module, and further obtain the concentration of the endogenous granular organic carbon by a regression analysis method; obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley values of the remote sensing reflectivity at the wavelength of 674 nm; and obtaining the concentration of the exogenous granular organic carbon according to the ratio of the endogenous granular organic carbon to the total organic carbon concentration and the endogenous granular organic carbon concentration, thereby realizing the monitoring of the granular organic carbon in the water body.
Specifically, in the particle organic carbon monitoring system, the water surface remote sensing reflectivity is measured by using a hyperspectral remote sensing sensor, and the hyperspectral remote sensing sensor can be a handheld field spectrum radiometer, a hyperspectral imager, an airborne remote sensing sensor, a satellite-borne remote sensing sensor and the like. In practical application, the satellite remote sensing image of the corresponding water area can be downloaded from the internet, for example, the satellite remote sensing image of the sentinel 3 OLCI sensor of the Hongze lake is downloaded from the internet, and then the corresponding OLCI data is subjected to atmospheric correction and calculated to obtain the water surface remote sensing reflectivity.
Endogenous granular organic carbon mainly comes from phytoplankton in a water body and has close correlation with the concentration of the phytoplankton, so that the phytoplankton can be used as an intermediate variable to invert the concentration. The phytoplankton absorption is a main factor causing light attenuation in the water body, and has a large influence on the remote sensing reflectivity of the water surface, so that the phytoplankton absorption coefficient can be estimated from the remote sensing reflectivity. The absorption of phycocyanin and chlorophyll in phytoplankton can cause the valley peak characteristics of the remote sensing reflectivity near 620nm (nanometer) and 709nm, so the absorption coefficient aph (620) of the phytoplankton at 620nm can be calculated according to the formula (1). For the measurement of the backscattering coefficient, in practical application, it can be observed by using instruments such as HS6 (HOBI Labs Hydroscat-6P HS-6 backscattering measurement instrument, USA), BB9 (backscattering instrument ECO-BB9, Wetlabs, USA) and the like.
After the absorption coefficient aph (620) of phytoplankton at 620nm is calculated, the measured concentration C of organic carbon of endogenous particles in a research area is utilizedendThe concentration C of the organic carbon of the endogenous particles can be obtained by carrying out regression analysis on the concentration C and the absorption coefficient aph (620)endAs shown in formula (2).
Because the ratio of organic carbon of the endogenous particles to the exogenous particles directly influences the sizes of the reflection peaks of the remote sensing reflectivities of 560nm and 709nm and the size of the reflection valley of 674nm, the depth of the envelope curve of the remote sensing reflectivities is calculated by utilizing the reflection peak values of the remote sensing reflectivities of 560nm and 709nm and the reflection peak valley value of the 674nm, and the regression equation is calculated by utilizing the actual measurement of the ratio of the endogenous particles to the exogenous particles, wherein the equation is shown as the formula (3). To be provided withThe ratio R of the organic carbon of the endogenous particles to the total organic carbon concentration is obtained according to calculationendTo obtain the total organic carbon concentration CpocAs shown in formula (4). Then calculating according to the formula (5) to obtain the organic carbon concentration of the exogenous particles as Cter。
In one example, a satellite remote sensing image of a sentinel 3 OLCI sensor of a Hongze lake is downloaded from the internet, and then atmospheric correction is carried out on corresponding OLCI data and the water surface remote sensing reflectivity is obtained through calculation; https:// scihub. copernius. eu), measuring the backscattering coefficient of the water body by using an HS6 sensor, and obtaining the concentration C of organic carbon in endogenous particles of the lake Hongze lake by adopting the methodendAs shown in FIG. 3 (in the diagram, the colors of the area A, the area B, the area C and the area D become lighter gradually, which means that the concentration of the organic carbon of the endogenous particles is lower and lower, the area A means that the concentration of the organic carbon of the endogenous particles is 0.4-0.6mg/L, the area B means that the concentration of the organic carbon of the endogenous particles is 0.3-0.4mg/L, the area C means that the concentration of the organic carbon of the endogenous particles is 0.2-0.3mg/L, and the area D means that the concentration of the organic carbon of the endogenous particles is 0.1-0.2mg/L), it can be seen from the figure that the concentration of the organic carbon of the endogenous particles is higher in the coastal area of the lake (the deeper the color, the higher the concentration of the organic carbon of the particles is), the center of the lake is relatively lower.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of program modules is illustrated, and in practical applications, the above-described distribution of functions may be performed by different program modules, that is, the internal structure of the apparatus may be divided into different program units or modules to perform all or part of the above-described functions. Each program module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one processing unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software program unit. In addition, the specific names of the program modules are only used for distinguishing the program modules from one another, and are not used for limiting the protection scope of the application.
Fig. 4 is a schematic structural diagram of a terminal device provided in an embodiment of the present invention, and as shown, the terminal device 200 includes: a processor 220, a memory 210, and a computer program 211 stored in the memory 210 and executable on the processor 220, such as: a particle organic carbon monitoring program based on hyperspectral remote sensing. When the processor 220 executes the computer program 211, the steps in each of the embodiments of the method for monitoring granular organic carbon based on hyperspectral remote sensing are implemented, or when the processor 220 executes the computer program 211, the functions of each module in each of the embodiments of the apparatus for monitoring granular organic carbon based on hyperspectral remote sensing are implemented.
The terminal device 200 may be a notebook, a palm computer, a tablet computer, a mobile phone, or the like. Terminal device 200 may include, but is not limited to, processor 220, memory 210. Those skilled in the art will appreciate that fig. 4 is merely an example of terminal device 200, does not constitute a limitation of terminal device 200, and may include more or fewer components than shown, or some components may be combined, or different components, such as: terminal device 200 may also include input-output devices, display devices, network access devices, buses, and the like.
The Processor 220 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor 220 may be a microprocessor or the processor may be any conventional processor or the like.
The memory 210 may be an internal storage unit of the terminal device 200, such as: a hard disk or a memory of the terminal device 200. The memory 210 may also be an external storage device of the terminal device 200, such as: a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device 200. Further, the memory 210 may also include both an internal storage unit of the terminal device 200 and an external storage device. The memory 210 is used to store the computer program 211 and other programs and data required by the terminal device 200. The memory 210 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by sending instructions to relevant hardware by the computer program 211, where the computer program 211 may be stored in a computer-readable storage medium, and when the computer program 211 is executed by the processor 220, the steps of the method embodiments may be implemented. Wherein the computer program 211 comprises: computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying the code of computer program 211, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the content of the computer readable storage medium can be increased or decreased according to the requirements of the legislation and patent practice in the jurisdiction, for example: in certain jurisdictions, in accordance with legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunications signals.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be construed as the protection scope of the present invention.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be construed as the protection scope of the present invention.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for persons skilled in the art, numerous modifications and adaptations can be made without departing from the principle of the present invention, and such modifications and adaptations should be considered as within the scope of the present invention.
Claims (10)
1. A particle organic carbon monitoring method based on hyperspectral remote sensing is characterized by comprising the following steps:
measuring a water surface spectrum through a hyperspectral remote sensing sensor, and further obtaining the water surface remote sensing reflectivity;
measuring an underwater spectrum through a backscattering type sensor to further obtain a backscattering coefficient of the water body;
obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity and the backscattering coefficient, and further obtaining the concentration of the endogenous granular organic carbon by a regression analysis method;
obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley values of the remote sensing reflectivity at the wavelength of 674 nm;
and obtaining the concentration of the exogenous granular organic carbon according to the ratio of the endogenous granular organic carbon to the total organic carbon concentration and the endogenous granular organic carbon concentration, thereby realizing the monitoring of the granular organic carbon in the water body.
2. The method for monitoring granular organic carbon according to claim 1, wherein in the step of obtaining the absorption coefficient of phytoplankton at the wavelength of 620nm according to the remote surface sensing reflectivity and the backscattering coefficient and further obtaining the concentration of endogenous granular organic carbon by a regression analysis method,
the phytoplankton absorption coefficient aph (620) at 620nm wavelength is:
wherein R isrs(indicates remote reflectance at nm wavelength, a)w(x) represents the absorption coefficient of pure water at a wavelength of nm, bb(778) Representing the backscattering coefficient of the water body at the wavelength of 778 nm;
endogenous particle organic carbon concentration CendComprises the following steps:
Cend＝v1×aph(620)+v2
where v1 and v2 represent regression equation coefficients.
3. The particulate organic carbon monitoring method according to claim 1 or 2, wherein in the ratio of the concentration of the endogenous particulate organic carbon to the total organic carbon obtained from the remote-sensing reflectance reflection peak at the wavelength of 560nm, 709nm and the remote-sensing reflectance peak-valley at the wavelength of 674nm, the ratio R isendComprises the following steps:
wherein a and b represent regression coefficients.
4. The particulate organic carbon monitoring method of claim 3, wherein in the obtaining of the exogenous particulate organic carbon concentration based on the ratio of the endogenous particulate organic carbon to the total organic carbon concentration and the endogenous particulate organic carbon concentration, the exogenous particulate organic carbon concentration is Cter：
Cter＝Cpoc-Cend
Wherein, CpocIs the total organic carbon concentration, and
5. a particle organic carbon monitoring system based on hyperspectral remote sensing is characterized by comprising:
the hyperspectral remote sensing sensor is used for measuring and obtaining a water surface spectrum;
the backscattering type sensor is used for measuring and obtaining an underwater spectrum;
terminal equipment of built-in granule organic carbon monitoring devices, built-in granule organic carbon monitoring devices includes:
the data acquisition module is in communication connection with the hyperspectral remote sensing sensor and the back scattering type sensor respectively and is used for acquiring the water surface spectrum and the underwater spectrum;
the water surface remote sensing reflectivity acquisition module is used for obtaining the water surface remote sensing reflectivity according to the water surface spectrum measured by the hyperspectral remote sensing sensor;
the backscattering coefficient acquisition module is used for obtaining the backscattering coefficient of the water body according to the underwater spectrum measured by the backscattering sensor;
the operation module is used for obtaining the absorption coefficient of the phytoplankton at the wavelength of 620nm according to the water surface remote sensing reflectivity obtained by the water surface remote sensing reflectivity obtaining module and the backscattering coefficient obtained by the backscattering coefficient obtaining module, and further obtaining the concentration of the organic carbon in the endogenous particles by a regression analysis method; obtaining the ratio of the organic carbon of the endogenous particles to the total organic carbon concentration according to the reflection peak values of the remote sensing reflectivity at the wavelengths of 560nm and 709nm and the reflection peak valley values of the remote sensing reflectivity at the wavelength of 674 nm; and obtaining the concentration of the exogenous granular organic carbon according to the ratio of the endogenous granular organic carbon to the total organic carbon concentration and the endogenous granular organic carbon concentration, thereby realizing the monitoring of the granular organic carbon in the water body.
6. The particulate organic carbon monitoring system of claim 5, wherein in the operational module, an absorption coefficient aph (620) of phytoplankton at a wavelength of 620nm is:
wherein R isrs(indicates remote reflectance at nm wavelength, a)w(x) represents the absorption coefficient of pure water at a wavelength of nm, bb(778) Representing the backscattering coefficient of the water body at the wavelength of 778 nm;
endogenous particle organic carbon concentration CendComprises the following steps:
Cend＝v1×aph(620)+v2
where v1 and v2 represent regression equation coefficients.
7. The particulate organic carbon monitoring system according to claim 5 or 6, wherein in the operation module, the ratio R of endogenous particulate organic carbon to total organic carbon concentrationendComprises the following steps:
wherein a and b represent regression coefficients.
8. The particulate organic carbon monitoring system of claim 7, wherein in the operational module, the exogenous particulate organic carbon concentration is Cter：
Cter＝Cpoc-Cend
Wherein, CpocIs the total organic carbon concentration, and
9. terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the method for monitoring particulate organic carbon based on hyperspectral remote sensing according to any of claims 1 to 4.
10. A computer-readable storage medium, which stores a computer program, wherein the computer program, when being executed by a processor, implements the steps of the method for monitoring particulate organic carbon based on hyperspectral remote sensing according to any of claims 1 to 4.
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