CN115290512A - Water color remote sensing method and device for estimating concentration of organic carbon in ocean surface particles - Google Patents
Water color remote sensing method and device for estimating concentration of organic carbon in ocean surface particles Download PDFInfo
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Abstract
The invention discloses a water color remote sensing method and a water color remote sensing device for estimating the concentration of organic carbon in ocean surface particles, and relates to the technical field of water quality parameter remote sensing inversion, wherein absorption coefficients at different wavelengths of all matching points in an optical information image are extracted; constructing a relation between actually measured organic carbon concentration and an absorption coefficient at each wavelength in a matching point data set, and performing multiple nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determining coefficient; taking the absorption coefficient at the wave band as an independent variable, actually measuring the particle organic carbon concentration as a dependent variable, and establishing a logarithmic model to obtain a particle organic carbon concentration remote sensing inversion model based on the absorption coefficient; and obtaining the organic carbon concentration of the ocean surface layer particles at different geographic positions by utilizing the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient and the remote sensing image of the satellite to be detected. The problem that the inversion value of the current particle organic carbon concentration inversion algorithm is low in a high-productivity area is solved, and the method has the advantages of wide application area and strong feasibility.
Description
Technical Field
The invention belongs to the field of water quality parameter remote sensing inversion, and relates to a water color remote sensing method and a water color remote sensing device for estimating the concentration of organic carbon in ocean surface particles.
Background
The ocean plays an important role in the global carbon cycle by regulating the concentration of carbon dioxide in the atmosphere. Carbon in the ocean can be classified into dissolved inorganic carbon, dissolved organic carbon, particulate inorganic carbon, and particulate organic carbon. Although particulate organic carbon accounts for only a small portion of the ocean's surface carbon pool, its importance is high turnover rate. The method is important for knowing the growth rate of the marine phytoplankton and the carbon-based net primary productivity by mastering the distribution information of the organic carbon concentration of the marine particles.
The traditional granular organic carbon ship measurement method is time-consuming and labor-consuming and is limited by time and space. The ocean water color remote sensing technology has the advantages of long time sequence and large-area observation, along with the continuous development of water color satellites, the space-time resolution of the water color sensor is obviously improved, and better support is provided for the observation of the organic carbon concentration of global ocean surface particles.
At present, various water color remote sensing inversion methods for organic carbon concentration of sea surface layer particles are established, most methods are established on limited regional actually-measured particle organic carbon data sets, and due to the diversity of global ocean water bodies, the regional methods are difficult to apply to global ocean areas, namely the applicability is poor.
Meanwhile, the wave band ratio (BG) algorithm is commonly adopted to obtain the global organic carbon concentration of the ocean surface particles at the present stage, and the algorithm is also applied to an L2 level product suite of a sensor such as a medium resolution imaging spectrometer (MODIS) by the American aerospace agency to provide the global organic carbon concentration. Although the band ratio method gives a reliable result in the inversion of the organic carbon concentration of the global open ocean surface grains, in a high-concentration organic carbon area, the inversion value obtained by the method is greatly lower than the measured value, namely the inversion accuracy is poor in a high-productivity area.
Disclosure of Invention
In view of the above, the invention provides a water color remote sensing method for estimating the concentration of organic carbon in particles on the ocean surface layer, so as to solve the technical problems of poor inversion accuracy and poor applicability of the existing method for inverting the organic carbon in the particles in the high-productivity area.
Therefore, the technical scheme adopted by the invention is as follows:
in one aspect, the invention provides a water color remote sensing method for estimating the concentration of organic carbon in ocean surface layer particles, which comprises the following steps:
acquiring a global ocean surface layer organic carbon concentration actual measurement data set;
acquiring an optical information image representing the global ocean surface water body;
carrying out time and space matching on the data of the organic carbon concentration actual measurement data set and the optical information image to obtain a matching point data set, and dividing the matching point data set into a matching point training set and a matching point testing set; the matching point training set is used for constructing a particle organic carbon concentration remote sensing inversion model based on an absorption coefficient; the matching point test set is used for verifying the precision of the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient;
extracting absorption coefficients at different wavelengths of all matching points in the optical information image;
constructing the relation between the actually measured organic carbon concentration and the absorption coefficient at each wavelength in the matching point data set, and performing multiple nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determining coefficient; taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band;
taking the absorption coefficient at the model wave band as an independent variable and actually measuring the particle organic carbon concentration as a dependent variable, and establishing a logarithmic model, wherein the logarithmic model is a particle organic carbon concentration remote sensing inversion model based on the absorption coefficient;
and obtaining the organic carbon concentration of the ocean surface layer particles at different geographic positions by utilizing the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be detected.
Further, the wavelength band corresponding to the absorption coefficient having the highest determination coefficient is 490nm (a (490)).
Further, the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient is as follows:
wherein X = log 10 a(490)。
Further, the time and space matching of the data of the measured particle organic carbon concentration data set and the optical information image includes: and taking the sampling time of the actually measured data of the particle organic carbon concentration and the transit time of the satellite as the same day, taking a pixel corresponding to the actually measured data sampling point as a central pixel, extracting 3 multiplied by 3 pixels around the pixel, and averaging.
Further, the optical information image for representing the global ocean surface water body comprises: OC-CCI remote sensing images.
Further, the satellite remote sensing image to be detected comprises: OC-CCI remote sensing image or OLCI L1B remote sensing image.
Further, when the remote sensing image of the satellite to be measured is an OLCI L1B remote sensing image, the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be measured are used for obtaining the organic carbon concentrations of the particles on the ocean surface layer at different geographic positions, and the method comprises the following steps:
obtaining an OLCI L1B remote sensing image;
performing geographic correction and atmospheric correction on the OLCI L1B remote sensing image;
extracting absorption coefficients of all matching points in the OLCI L1B remote sensing image under the model wave band by adopting a QAA _ V5 semi-analytical algorithm;
and obtaining the ocean surface layer particle organic carbon concentrations at different geographic positions in the OLCI L1B remote sensing image based on the absorption coefficient and the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient.
Further, when the remote sensing image of the satellite to be detected is an OC-CCI remote sensing image, the organic carbon concentration of the ocean surface layer particles at different geographic positions is obtained by using the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient and the remote sensing image of the satellite to be detected, and the method comprises the following steps:
obtaining an OC-CCI remote sensing image;
extracting absorption coefficients of all matching points in the OC-CCI remote sensing image under the model wave band;
and obtaining the ocean surface layer particle organic carbon concentrations at different geographic positions in the OC-CCI remote sensing image based on the absorption coefficient and the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient.
In another aspect, the present invention further provides a water color remote sensing device for estimating the organic carbon concentration of ocean surface layer particles, comprising:
a first acquisition module: acquiring a global ocean surface layer organic carbon concentration actual measurement data set;
a second obtaining module: acquiring an optical information image representing the global ocean surface water body;
a matching module: carrying out time and space matching on the data of the organic carbon concentration actual measurement data set and the optical information image to obtain a matching point data set, and dividing the matching point data set into a matching point training set and a matching point testing set; the matching point training set is used for constructing a particle organic carbon concentration remote sensing inversion model based on an absorption coefficient; the matching point test set is used for verifying the precision of the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient;
an extraction module: extracting absorption coefficients of all matching points in the optical information image at different wavelengths;
constructing a module: constructing the relation between the actually measured organic carbon concentration and the absorption coefficient at each wavelength in the matching point data set, and performing multiple nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determining coefficient; taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band;
and establishing a logarithmic model by taking the actually measured organic carbon concentration as an independent variable and the absorption coefficient at the model wave band section as a dependent variable, wherein the logarithmic model is a remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient.
An estimation module: and obtaining the organic carbon concentration of the ocean surface layer particles at different geographic positions by utilizing the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be detected.
The water color remote sensing method for estimating the concentration of the organic carbon in the ocean surface layer particles is combined with a global particle organic carbon shared data set to establish a new method for estimating the concentration of the organic carbon in the ocean surface layer particles, so that the accurate representation of the concentration of the organic carbon in the global ocean surface layer particles is realized, and the adaptability of a scheme is enhanced; compared with a wave band ratio (BG) algorithm, in a low-concentration particle organic carbon area, the inversion result similar to the low-concentration particle organic carbon area is obtained; however, in a high concentration region, the present invention provides a result more consistent with the actual measurement value. According to the method, a particle organic carbon concentration remote sensing inversion model based on the inherent optical quantity of the water body is established, and a global ocean surface particle organic carbon concentration product is obtained by utilizing satellite data, so that the problem that the inversion value of the current particle organic carbon concentration inversion algorithm is low in a high-productivity area can be solved, and a more accurate result is provided for further estimating the global ocean carbon reserve. Has the advantages of wide applicable area, strong feasibility and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a technical roadmap for the present invention;
FIG. 2 is a constructed fit chart of the method of the present invention;
FIG. 3 (a) is a scatter plot of POC concentration inverted versus measured values obtained by the BG algorithm; (b) The POC concentration inversion value and the measured value obtained by the algorithm are scattered point diagrams;
FIG. 4 is a diagram of global monthly POC products generated using OC-CCI monthly average video in accordance with the present invention;
FIG. 5 (a) is a region of interest map; (b) The algorithm provided by the invention is applied to an OLCI image for inverting a POC concentration map I in Bohai sea; (c) The algorithm provided by the invention is applied to the inversion of a POC concentration graph II of an OLCI image in the Bohai sea; (d) The algorithm provided by the invention is applied to the inversion of the POC concentration graph III of the OLCI image in the Bohai sea.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a water color remote sensing method for estimating the concentration of organic carbon in ocean surface particles provided in an embodiment of the present invention includes the following steps:
s1: acquiring measured concentration data of Particle Organic Carbon (POC);
the POC concentration measured data is a global ocean surface layer POC concentration measured data set, and in the specific implementation, the global ocean surface layer POC concentration shared measured data set is obtained from SeaBASS (https:// seaabass.gsfc.nasa.gov /), BCO-DMO (https:// www.bco-dmo.org /) and PANGAEA (https:// www.pangaea.de /); besides, on-site sampling and laboratory measurement are carried out, and actual measurement data of the POC concentration are obtained.
S2: acquiring an optical information image representing the global ocean surface water body;
in a specific implementation, OC-CCI (Ocean Color close Change Initiative) v5.0 global hybrid satellite remote sensing image all _ products can be downloaded from Ocean Color (https:// close. Esa. Int/en/projects/Ocean-Color /).
S3: carrying out time and space matching on data in the POC measured data set and an optical information image to obtain a plurality of matching points to form a matching point data set, and dividing the matching point data set into a training set and a testing set;
the requirements for matching are: and taking the actually measured data sampling time and the satellite transit time as the same day, taking a pixel corresponding to the actually measured data sampling point as a central pixel, extracting 3 multiplied by 3 pixels, and averaging.
In a specific implementation, 2/3 of the points in the matching point data set can be used as a training set, and the rest can be used as a testing set.
S4: and extracting the absorption coefficients a (lambda) of all the matching points at different wavelengths in the optical information image of the global ocean surface water body.
S5: constructing a relation between the actual measurement POC concentration in the training set and the absorption coefficient a (lambda) at each wavelength, performing multivariate nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest decision coefficient, and taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band; taking the absorption coefficient at the model wave band as an independent variable and actually measuring the POC concentration as a dependent variable, and establishing a logarithmic model, wherein the logarithmic model is a POC concentration remote sensing inversion model based on the absorption coefficient;
in the embodiment of the present invention, the coefficient R is determined 2 The wavelength band lambda corresponding to the highest absorption coefficient a (lambda) is 490nm.
Extracting the absorption coefficient a (490) at 490nm of all matching points from the optical information image of the global ocean surface water body by using SNAP (Sentinel Applications Platform) software;
as shown in fig. 2, the expressions for the POC concentrations at the ocean surface layer at different geographic locations are as follows:
wherein X = log 10 a(490)。
Formula (1) is named as a-POC algorithm.
As shown in fig. 3, the a-POC algorithm and the NASA official POC algorithm BG algorithm are applied to the precision validation matching point data, fig. 3 (a) and (b) are precision relationships between the BG algorithm and the a-POC algorithm, respectively, and the measured POC concentration, and the black solid line represents 1:1 line and a dotted line represent a Model-II type linear regression line with a linear fitting relation and a coefficient of determination R 2 。
Compared with the BG algorithm, the a-POC algorithm has a slope closer to 1 and an intercept closer to 0, and the root mean square errors of the two algorithms are 128.49mg/m respectively 3 And 184.48mg/m 3 Deviation of-3.29 mg/m respectively 3 And-49.06 mg/m 3 。
S6: and obtaining the POC concentration of the ocean surface layer at different geographic positions by utilizing the POC concentration remote sensing inversion model based on the absorption coefficient and the satellite remote sensing image to be detected.
In specific application, the satellite remote sensing image to be detected can be an OC-CCI image, specifically, OC-CCI image a (490) data synchronized with the time and space of the measured data is extracted, and the a-POC algorithm is applied to obtain the POC concentration of the ocean surface layer at different geographic positions.
The a-POC algorithm was applied to the OC-CCI average image in 5 months of 2020, and Python tool was used to estimate the POC concentration of each pixel, and a global POC concentration map was generated, as shown in FIG. 4.
The satellite remote sensing image to be detected can also be an OLCI L1B remote sensing image, and the method specifically comprises the following steps:
s61: collecting data, namely collecting a water sample on the Bohai sea shore and measuring the concentration of POC in a laboratory;
s62: downloading a satellite remote sensing image, and downloading an OLCI (Ocean and Land Colour Instrument) research area L1B full-resolution (300 m) remote sensing image corresponding to data sampling time;
s63: preprocessing a satellite remote sensing image, firstly carrying out atmospheric correction processing on the L1B image, and showing that a POLYMER atmospheric correction algorithm is more suitable for the Bohai sea near shore through earlier research, so that atmospheric correction is carried out on the L1B image by using POLYMER v4.13 to generate an L2 remote sensing reflectivity Rrs (lambda);
s64: calculating a (490) by using an QAA _ V5 semi-analytical algorithm and Rrs (lambda), wherein the calculating steps are as follows:
wherein g is 0 ＝0.089，g 1 ＝0.125。
S65: and extracting OLCI L1B remote sensing image a (490) data which is synchronous with the time and space of the actually measured data, and obtaining the POC concentration of the ocean surface layer at different geographic positions by applying an a-POC algorithm.
Compared with the NASA official POC algorithm BG algorithm, the median absolute percentage error of the a-POC algorithm is reduced from 40.77 percent to 19.13 percent, and the root mean square error is reduced from 66.26mg/m 3 Reduced to 35.97mg/m 3 Deviation of-297.80 mg/m 3 Reduced to-89.04 mg/m 3 . The problem that the inversion accuracy of the BG algorithm in the near-shore water area is low is obviously improved by the a-POC algorithm.
Respectively applying an a-POC algorithm to images in Bohai regions of OLCI 2017, 6, 14, 9, 19 and 23, estimating the POC concentration of each pixel point by using Python, and generating a POC concentration distribution diagram of the Bohai region of Qinhuang island, (b) applying the algorithm provided by the invention to an OLCI image in a Bohai region inversion POC concentration diagram I; (c) The algorithm provided by the invention is applied to the inversion of a POC concentration graph II of an OLCI image in Bohai sea; (d) The algorithm provided by the invention is applied to the inversion of a POC concentration map III of OLCI images in Bohai sea. In fig. 5, the scatter dots are the positions of the actually measured sampling points, and the POC concentrations are displayed by the color bars below the background colors and the scatter dots corresponding to the colors.
Corresponding to the water color remote sensing method for estimating the ocean surface layer POC concentration in the embodiment, the embodiment of the invention also provides a water color remote sensing device for estimating the ocean surface layer POC concentration, which comprises the following steps:
a first acquisition module: acquiring a global ocean surface layer POC concentration measured data set;
a second obtaining module: acquiring an optical information image representing the global ocean surface water body;
a matching module: carrying out time and space matching on the data of the POC concentration actual measurement data set and the optical information image to obtain a matching point data set, and dividing the matching point data set into a matching point training set and a matching point testing set; the matching point training set is used for constructing a POC concentration remote sensing inversion model based on an absorption coefficient; the matching point test set is used for verifying the precision of the POC concentration remote sensing inversion model based on the absorption coefficient;
an extraction module: extracting absorption coefficients of all matching points in the optical information image at different wavelengths;
constructing a module: constructing the relation between the actually measured POC concentration and the absorption coefficient at each wavelength in the matching point data set, and performing multivariate nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determination coefficient; taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band; establishing a logarithmic model by taking the actually measured POC concentration as an independent variable and the absorption coefficient at the model wave band as a dependent variable, wherein the logarithmic model is a POC concentration remote sensing inversion model based on the absorption coefficient;
an estimation module: and obtaining the POC concentration of the ocean surface layer at different geographic positions by utilizing the POC concentration remote sensing inversion model based on the absorption coefficient and the satellite remote sensing image to be detected.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A water color remote sensing method for estimating the concentration of organic carbon in ocean surface particles is characterized in that: the method comprises the following steps:
acquiring a measured data set of the concentration of organic carbon in global ocean surface particles;
acquiring an optical information image representing the global ocean surface water body;
carrying out time and space matching on the data of the organic carbon concentration actual measurement data set and the optical information image to obtain a matching point data set, and dividing the matching point data set into a matching point training set and a matching point testing set; the matching point training set is used for constructing a particle organic carbon concentration remote sensing inversion model based on an absorption coefficient; the matching point test set is used for verifying the precision of the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient;
extracting absorption coefficients of all matching points in the optical information image at different wavelengths;
constructing the relation between the actually measured organic carbon concentration and the absorption coefficient at each wavelength in the matching point data set, and performing multivariate nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determination coefficient; taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band;
taking the absorption coefficient at the model wave band as an independent variable and actually measuring the particle organic carbon concentration as a dependent variable, and establishing a logarithmic model, wherein the logarithmic model is a particle organic carbon concentration remote sensing inversion model based on the absorption coefficient;
and obtaining the organic carbon concentration of the ocean surface layer particles at different geographic positions by utilizing the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be detected.
2. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 1, wherein the method comprises the following steps: the band corresponding to the absorption coefficient with the highest coefficient of determination is 490nm (a (490)).
3. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 2, wherein the method comprises the following steps: the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient is as follows:
wherein X = log 10 a(490)。
4. The water color remote sensing method for estimating the concentration of organic carbon in ocean surface grains according to claim 1, wherein the method comprises the following steps: and performing time and space matching on the data of the measured particle organic carbon concentration data set and the optical information image, wherein the time and space matching comprises the following steps: and taking the sampling time of the actually measured data of the particle organic carbon concentration and the transit time of the satellite as the same day, taking a pixel corresponding to the actually measured data sampling point as a central pixel, extracting 3 multiplied by 3 pixels around the pixel, and averaging.
5. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 1, wherein the method comprises the following steps: the optical information image for representing the global ocean surface water body comprises: OC-CCI remote sensing images.
6. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 1, wherein the method comprises the following steps: the satellite remote sensing image to be detected comprises: OC-CCI remote sensing image or OLCI L1B remote sensing image.
7. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 6, wherein the method comprises the following steps: when the remote sensing image of the satellite to be detected is an OLCIL1B remote sensing image, the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be detected are utilized to obtain the organic carbon concentrations of the particles on the ocean surface layer at different geographic positions, and the method comprises the following steps:
obtaining an OLCI L1B remote sensing image;
performing geographic correction and atmospheric correction on the OLCIL1B remote sensing image;
extracting absorption coefficients of all matching points in the OLCI L1B remote sensing image under the model wave band by adopting a QAA _ V5 semi-analytical algorithm;
and obtaining the concentration of the organic carbon in the ocean surface layer particles at different geographic positions in the OLCIL1B remote sensing image based on the absorption coefficient and the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient.
8. The method for remotely sensing the water color for estimating the concentration of organic carbon in ocean surface particles according to claim 6 or 7, wherein the method comprises the following steps: when the satellite remote sensing image to be detected is an OC-CCI remote sensing image, obtaining the ocean surface layer particle organic carbon concentrations at different geographic positions by utilizing the absorption coefficient-based particle organic carbon concentration remote sensing inversion model and the satellite remote sensing image to be detected, wherein the method comprises the following steps:
obtaining an OC-CCI remote sensing image;
extracting absorption coefficients of all matching points in the OC-CCI remote sensing image under the model wave band;
and obtaining the ocean surface layer particle organic carbon concentrations at different geographic positions in the OC-CCI remote sensing image based on the absorption coefficient and the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient.
9. A water color remote sensing device for estimating the organic carbon concentration of ocean surface particles is characterized in that: the method comprises the following steps:
a first obtaining module: acquiring a global ocean surface layer organic carbon concentration actual measurement data set;
a second obtaining module: acquiring an optical information image representing the global ocean surface water body;
a matching module: carrying out time and space matching on the data of the organic carbon concentration actual measurement data set and the optical information image to obtain a matching point data set, and dividing the matching point data set into a matching point training set and a matching point testing set; the matching point training set is used for constructing a particle organic carbon concentration remote sensing inversion model based on an absorption coefficient; the matching point test set is used for verifying the precision of the remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient;
an extraction module: extracting absorption coefficients at different wavelengths of all matching points in the optical information image;
constructing a module: constructing the relation between the actually measured organic carbon concentration and the absorption coefficient at each wavelength in the matching point data set, and performing multivariate nonlinear regression fitting to obtain a wave band corresponding to the absorption coefficient with the highest determination coefficient; taking the wave band corresponding to the absorption coefficient with the highest decision coefficient as a model wave band; taking the actually measured organic carbon concentration as an independent variable and the absorption coefficient at the model wave band section as a dependent variable, and establishing a logarithmic model, wherein the logarithmic model is a remote sensing inversion model of the particle organic carbon concentration based on the absorption coefficient;
an estimation module: and obtaining the organic carbon concentration of the ocean surface layer particles at different geographic positions by utilizing the particle organic carbon concentration remote sensing inversion model based on the absorption coefficient and the remote sensing image of the satellite to be detected.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116908114A (en) * | 2023-09-07 | 2023-10-20 | 水利部交通运输部国家能源局南京水利科学研究院 | Remote sensing monitoring method for river basin granule organic carbon flux |
| CN117592316A (en) * | 2024-01-18 | 2024-02-23 | 自然资源部第二海洋研究所 | Sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116908114A (en) * | 2023-09-07 | 2023-10-20 | 水利部交通运输部国家能源局南京水利科学研究院 | Remote sensing monitoring method for river basin granule organic carbon flux |
| CN116908114B (en) * | 2023-09-07 | 2023-12-01 | 水利部交通运输部国家能源局南京水利科学研究院 | Remote sensing monitoring method for river basin granule organic carbon flux |
| CN117592316A (en) * | 2024-01-18 | 2024-02-23 | 自然资源部第二海洋研究所 | Sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation |
| CN117592316B (en) * | 2024-01-18 | 2024-04-05 | 自然资源部第二海洋研究所 | Sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation |
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