CN115950855A - Chlorophyll a concentration inversion method for medium-low nutrient water body - Google Patents
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Abstract
The invention discloses a chlorophyll a concentration inversion method for medium and low nutrient water bodies, which relates to the technical field of optical remote sensing and comprises the following steps: acquiring a plurality of earth surface reflectivity images; acquiring reflectivity data of the medium and low nutrient water body according to a plurality of surface reflectivity images, and calculating a plurality of classification wave band reflectances; classifying the medium-low nutrient water body into a plurality of different water body types according to a plurality of classification wave band reflectance ratios; selecting different inversion wave bands for various water body types, calculating the reflection ratio of the inversion wave bands according to the different inversion wave bands, establishing a polynomial wave band ratio inversion model according to the reflection ratio of the inversion wave bands, and inverting the chlorophyll a concentrations of the various water body types. The invention provides a simple wave band ratio water body classification algorithm based on the earth surface reflectivity image, classifies low and medium nutrient water bodies, establishes a different water body wave band ratio mixing algorithm, improves the chlorophyll a inversion precision, and is suitable for small and medium size reservoirs or lakes of low and medium nutrient water bodies.
Description
Technical Field
The invention relates to the technical field of optical remote sensing, in particular to a chlorophyll a concentration inversion method for a medium-low nutrient water body.
Background
The reservoir has wide distribution range, generally has comprehensive functions of flood control, drought resistance, power generation, irrigation, urban and industrial water supply and the like, is an important water source area and a reserve water source area, and plays a vital role in ecology, society and economy. As one kind of artificial lake, the reservoir has the characteristics of both rivers and lakes, has the characteristics of low updating speed and relatively weak dilution self-purification capacity, so that the water quality safety of the reservoir becomes a common concern of supervision departments and scientific researchers, particularly for water source type reservoirs. The chlorophyllin a concentration (Cchl-a) of phytoplankton has been used as an important indicator of the ecological integrity of aquatic ecosystems, and although a moderate number of phytoplankton is critical to aquatic ecosystems, its excess may be detrimental to ecosystem function and public health. The traditional actual measurement method cannot accurately monitor the water quality of all lakes in a spatial scale, and optical remote sensing has been used as an effective water quality observation method for a long time, so that the key physical-biological process and water weather in a water body ecosystem can be economically and rapidly observed.
The principle of optical remote sensing monitoring Cchl-a is that the inherent optical characteristics of the water body are obtained through an optical remote sensing sensor, namely, the atmospheric effect is removed from a remote sensing signal recorded on the top of the atmosphere, the remote sensing signal is simplified into remote sensing reflectivity, the optical characteristics of the water body are quantized, and then Cchl-a is estimated. The phytoplankton reflectance is characterized mainly by the formation of distinct reflectance peaks near 560nm and 700nm, and the features at 675nm can offset 700nmnear infrared scattering peak around nm. Depending on the phytoplankton absorption and backscattering characteristics, a band combination semi-empirical algorithm using the blue-green band or the red and near-infrared bands is formed. The blue-green ratio algorithm is suitable for a class of water bodies only affected by phytoplankton and decomposition products thereof, is developed into a mature ocean color series algorithm, is applied to Cchll-a estimation of the global ocean, can estimate Cchll-a in clear water with high precision, but often overestimates Cchll-a in inland and coastal water areas. The red-near infrared method assumes that the absorption of colored dissolved organic matters and non-algae particles can be ignored, is not sensitive to uncertainty in atmospheric correction, is suitable for two types of water bodies strongly influenced by debris substances or colored dissolved organic matters, and is applied to the inversion Cchll-a of the MERIS spectral band. In addition, a three-band model and a four-band model are established for the turbid eutrophic water body. And a TC2 model is established for the high-turbidity water body. An SCI index model is developed for a water body with high suspended sediment and low chlorophyll. A fluorescence algorithm is developed according to the change of a reflection peak near 700nm of phytoplankton, and the basic characteristics of Cchl-a absorption and fluorescence peak in a 665-685nm region. And some algorithms and intelligent algorithms based on inversion of Intrinsic Optical Properties (IOP). It became clear that the blue-green ratio was limited to the nutritionally poor waters, and that the red-near infrared ratio was only applicable to Cchl-a concentrations greater than 10mg.m -3 The water area of (2).
Cchl-a inversion algorithm for different optical property water areas can achieve higher precision, and a switching and mixing multiple algorithms or a weighting integration scheme is superior to a single algorithm. And performing water body classification by optical classification of waveform characteristics of the remote sensing reflectivity, and analyzing and clustering analysis of functional data such as a k-mean method, fuzzy C-mean clustering Fuzzy C-Means and the like. Raw remote reflectance data collected from 185 inland and coastal water systems worldwide (n = 2807) as collected by Neil (2019) divides water into 13 different Optical Water Types (OWTs). Each OWT is associated with a different bio-optical property and readjusting the algorithm to optimize the parameterization (i.e. algorithm, parameterizations) of each individual OWT can improve the overall inversion accuracy of Cchl-a. The classification according to the inherent optical characteristics comprises that Sunweiyong takes a refractive index np as an index to divide the water body into phytoplankton and inorganic particlesThe object dominates and the water area dominated by both. According to the ratio of phytoplankton pigment-based and non-pigment-based particle absorption aph (443)/ad (443), the water area category is divided into debris and pigment-based water areas and intermediate water areas, and according to the absorption and reflection characteristics of phytoplankton, the water areas in 3 states are distinguished by using CI672 and CI 555. Gomez et al (2011) proposes that two normalized difference indices (i.e., 705nm and 665nm bands, 560nm and 442nm bands) classify the european union's mediterranean lakes into two types. Matsushita et al (2015) used different thresholds for Maximum Chlorophyl Index (MCI) to divide water into 10mg.m -3 And 25mg.m -3 Three water bodies with different nutrition states.
In conclusion, the water body classification and the Cchl-a inversion algorithm have been verified in some inland water areas, but the application of the algorithms in other water areas has limitations, and the research is very limited especially for some small and medium-sized low-nutrition and medium-nutrition inland reservoir water bodies. The water area of the MEdium and small size reservoirs is not large, and the space Resolution which is applied to the MEdium and low satellites such as Visible acquired Imaging Radiometer Suite, ocean and Land color Instrument, MEdium Resolution Imaging Spectrometer and the like in the past is difficult to apply, so that the remote sensing monitoring of the water body is insufficient. In recent decades, most inland water quality researches are concentrated on eutrophic water bodies, the inversion method is relatively few and the inversion accuracy uncertainty is higher for insufficient water body sampling of medium-nutrient and low-nutrient water bodies Cchl-a, and the development and verification of the Cchl-a estimation method are limited. The Sentinel-2 satellite has the highest spatial resolution of 10m and the revisit period of 5 days, can well solve the problem of low resolution of the conventional satellite, is considered to be the satellite most suitable for remote sensing inversion of inland water bodies, but due to the problem of wave band setting, wave bands such as 555nm, 672nm, 708nm and 751nm involved in a mature algorithm do not exist, and research on water body classification of the satellite and Cchl-a inversion methods applicable to different water bodies is less.
Disclosure of Invention
The invention provides a chlorophyll a concentration inversion method for a medium-low nutrient water body, which can solve the problems in the prior art.
The invention provides a chlorophyll a concentration inversion method of a medium-low nutrient water body, which comprises the following steps:
acquiring a plurality of earth surface reflectivity images;
obtaining reflectivity data of the medium and low nutrient water body according to the plurality of surface reflectivity images, and calculating a plurality of classification wave band reflectances according to the reflectivity data;
classifying the medium-low nutrient water body into a plurality of different water body types according to a plurality of classification wave band reflectance ratios;
selecting different inversion wave bands for various different water body types, calculating the reflection ratio of the inversion wave bands according to the different inversion wave bands, establishing a polynomial wave band ratio inversion model according to the reflection ratio of the inversion wave bands, and inverting the chlorophyll a concentrations of the various different water body types through the polynomial wave band ratio inversion model;
the polynomial band ratio inversion model is as follows:
Cchl-a＝a*BR(i) 2 +b*BR(i)+c
wherein Cchl-a is chlorophyll a concentration, BR (i) is the wave band reflectance of different water body types, and a, b and c are constants.
Preferably, a plurality of earth surface reflectivity images are acquired based on the sentinel 2 satellite, the sentinel 2 satellite carries a multispectral imager, radiation measurement is obtained from 8 spectral bands from visible light to near infrared, and the central wavelengths of the radiation measurement are 443nm, 490nm, 560nm, 665nm, 705nm, 740nm, 783nm and 842nm respectively.
Preferably, before the reflectivity data of the medium-low nutrient water body is acquired, the acquired multiple earth surface reflectivity images need to be subjected to atmospheric correction and nearest neighbor resampling pretreatment; the atmospheric correction is based on the libidtran radiative transfer model in the Sen2Cor processor, and the nearest neighbor resampling converts the spatial resolution of the spectral band from 20 meters to 10 meters.
Preferably, a surface feature spectrometer is used for obtaining the reflectivity data of the medium and low nutrient water body from the plurality of surface reflectivity images.
Preferably, the reflectance data includes band data at 490nm, 560nm, 665nm, 705nm, and 842nm.
Preferably, the medium-low nutrient water body is classified into various different water body types according to the reflection ratios of a plurality of wave bands, and the method specifically comprises the following conditions:
when R490/R560 is not less than 0.8, the water body is type 1;
when R490/R560 is less than 0.8 and R665/R560 is not less than 0.6, the water body is type 2;
when R490/R560 is less than 0.8 and R665/R560 is less than 0.6, the water body is type 3,
wherein R is the reflectance.
Preferably, the type 1 is a clear water body, the type 2 is a water body dominated by phytoplankton, and the type 3 is a water body dominated by phytoplankton and other substances.
Preferably, different inversion wave bands are selected for different water body types, and the inversion wave band reflectance is calculated according to the different inversion wave bands, specifically comprising the following conditions:
the type 1 selects 665nm and 490nm, and the reflection ratio of the inversion waveband is R665/R490;
705nm and 560nm are selected as the type 2, and the reflection ratio of the inversion waveband is R705/R560;
the type 3 is selected from 842nm and 665nm, and the reflection ratio of the inversion waveband is R842/R665.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a simple wave band ratio water body classification algorithm based on an actually measured spectrum and a ground surface reflectivity image, classifies low and medium nutrient water bodies, establishes a different water body wave band ratio mixing algorithm, improves the chlorophyll a inversion precision, and is suitable for small and medium size reservoirs or lakes of low and medium nutrient water bodies.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an inversion method of chlorophyll a concentration of a medium-low nutrient water body according to the invention;
FIG. 2 is a flow chart of an inversion method of chlorophyll a concentration of a medium-low nutrient water body according to the invention;
FIG. 3 (a) is a schematic diagram of the discrimination between different bodies of water based on the reflectance of the R490/R560 band;
FIG. 3 (b) is a schematic diagram of the discrimination between different bodies of water based on the reflectance of the R665/R560 band;
FIG. 4 is a remote sensing reflectance spectrum of three water bodies according to the present invention;
FIG. 5 is a diagram of the inversion results of Cchl-a for three bodies of water according to the present invention;
FIG. 6 (a) is a graph of MCI vs. Cchl-a;
FIG. 6 (b) is a graph of BR vs. Cchl-a;
FIG. 6 (c) is a graph of TBA versus Cchl-a;
FIG. 7 (a) is a graph comparing the inversion results and actual measurement results of MCI and the classical algorithm;
FIG. 7 (b) is a comparison graph of inversion results and actual measurement results of BR and classical algorithms;
fig. 7 (c) is a graph comparing the inversion results and actual measurement results of TBA and the classical algorithm.
Detailed Description
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.
Referring to fig. 1, the invention provides a chlorophyll a concentration inversion method of a medium and low nutrient water body, based on Sentinel-2 high resolution (10 m) satellite data and actually measured remote sensing reflectivity data, by using medium and small reservoirs in the middle region of China: a Luzhou lake reservoir, a small wave bottom reservoir (southeast), a Hold duck lake reservoir and a Danjiang mouth reservoir (Danjiang reservoir area) are research areas, and a mixing algorithm suitable for inversion of chlorophyll a concentrations of different nutritional water bodies (ultra-oligotrophic, oligotrophic and mesotrophic water bodies) is researched. The water body is divided into three types by a single actual measurement remote sensing reflectivity ratio method, then a two-waveband reflectance empirical model suitable for each water body type is established, and the chlorophyll a concentration inversion accuracy of different water body types is improved. The method specifically comprises the following steps:
the first step is as follows: multiple earth surface reflectance images are acquired.
Sentinel 2 (Sentinel-2) is an earth observation task under the plan of Copenny of the European space agency, and consists of 2 same satellite sentinels 2A (Sentinel-2A) and B (Sentinel-2B), so that 5-day global revisitation can be realized. Each satellite carries the same Multi-spectral imager (MSI). The imager can obtain radiometric measurements from 8 spectral bands from the visible to the near infrared, with different spatial resolutions, with center wavelengths 443 (60 m), 490 (10 m), 560 (10 m), 665 (10 m), 705 (20 m), 740 (20 m), 783 (20 m) and 842nm (10 m), respectively. The invention obtains 7 Level-2A surface reflectivity images corresponding to 2020-2022 years.
And performing atmospheric correction and nearest neighbor resampling pretreatment on the acquired image. The atmospheric correction is based on the libidtran radiative transfer model in the Sen2Cor processor. The spatial resolution of the spectral band is converted from 20 meters to 10 meters using nearest neighbor resampling.
The second step is that: and acquiring the reflectivity data of the water body from the plurality of surface reflectivity images by using a surface feature spectrometer, wherein the reflectivity data comprises the wave band data of 490nm, 560nm, 665nm, 705nm and 842nm.
And calculating a plurality of classification wave band reflection ratios, namely wave band reflection ratio values according to the reflection data, and distinguishing a plurality of water body types according to the plurality of classification wave band reflection ratios.
Most optical classification methods based on remote sensing reflectivity directly utilize specific characteristics of remote sensing spectra to classify optical complex water areas into different optical types. Due to weak absorption and cell scattering effects of chlorophyll a and carotene, a first reflection peak of the water body is located between 530 nm and 580nm and is increased along with the increase of the concentration of chlorophyll a, the reflectivity of the water body with low concentration of chlorophyll a in a blue light wave band is larger than that of other entities, so that the ratio of the blue light wave band to a peak wave band can be used for effectively distinguishing the water body with low concentration of chlorophyll a from other water bodies, and the basis for extracting the concentration of marine chlorophyll a by adopting a blue-green wave band ratio method is also provided. The other two water bodies have similar spectral shapes, except for obvious change in magnitude, the two water bodies both decline after reaching a peak value at 560nm, one water body declines rapidly from 560nm to 665nm and then gradually approaches 0, and the other water body declines in a step manner, so that the two water bodies are distinguished through the intensity of the reduction at 665 nm.
Referring to fig. 2-4, the invention distinguishes three types of water bodies based on the reflectance ratio of the R490/R560 and R665/R560 wave bands, and specifically includes the following conditions:
when R490/R560 is not less than 0.8, the water body is type 1;
when R490/R560 is less than 0.8 and R665/R560 is not less than 0.6, the water body is type 2;
when R490/R560 is less than 0.8 and R665/R560 is less than 0.6, the water body is type 3.
The third step: selecting different inversion wave bands for the various water body types after being distinguished, calculating the reflection ratio of the inversion wave bands according to the different inversion wave bands, establishing a polynomial wave band ratio inversion model according to the reflection ratio of the inversion wave bands, and inverting the chlorophyll a concentrations of the different water body types through the polynomial wave band ratio inversion model.
Empirical methods establish a relationship between optical measurements and component concentrations based on experimental data. They are easy to develop and implement, but their inherent design makes them particularly sensitive to compositional variations in the water composition. Therefore, different optical wave bands are selected for different types of water bodies. Since the use of the band ratio partially eliminates the effect of bi-directional changes in reflectivity. Therefore, the present invention uses the reflectance ratio to develop the inversion algorithm of Chl-a. Considering the specific wave bands of Sentinel, the inversion wave bands selected for the 3 bodies of water are b665/b490, b705/b560, and b842/b665, respectively. With the increasing turbidity degree of the water body, the selection of the molecular and denominator wave bands moves to the long wave band.
The polynomial band ratio inversion model is as follows:
Cchl-a＝a*BR(i) 2 +b*BR(i)+c
wherein Cchl-a is chlorophyll a concentration, BR (i) is band reflectance, and a, b and c are constants.
Referring to table 1, a polynomial band ratio inversion model is established by using b665/b490, b705/b560 and b842/b665, and chlorophyll a inversion results of three water bodies are obtained, as shown in fig. 5.
Table 1 two-band ratio chlorophyll a inversion model corresponding to different water body types
TABLE 2 inversion results (MCI, BR, TBA) of known classification methods and classical algorithms compared to the inversion results of this study
Referring to fig. 6, fig. 7 and table 2, the applicability of the classification and inversion model proposed by the present invention in low and medium nutrient water bodies is demonstrated by comparing with known classification models (maximum chlorophyl index (MCI), two band ratio method (BR) and Three Band Algorithm (TBA)). The inversion accuracy of each water body is higher than that of other classical algorithms, the average absolute error is minimum, and the overall deviation is not more than 1mg/m 3 。
Lakes and reservoirs are important components of inland water bodies, and the inversion accuracy of the conventional medium-low resolution satellite with the mature chlorophyll a concentration inversion algorithm is greatly reduced when the conventional medium-low resolution satellite aims at the canyon type reservoir with small area. And most algorithms aim at eutrophic water bodies, taking Henan province in cities in the middle of China as an example, and the eutrophication level of reservoirs in the whole province is medium nutrition. However, the inversion uncertainty of the existing algorithm for low and medium nutrient water bodies is higher than that of eutrophic water bodies. Therefore, the invention provides the chlorophyll a concentration inversion algorithm suitable for a high-precision resolution satellite aiming at low and medium nutrient water bodies, is simple and efficient, and is more suitable for small and medium-sized reservoirs and lakes in inland at present. Not only can enrich the water color parameter inversion theories and methods of different types of water bodies, but also has important practical significance for monitoring, managing and treating the water environment of eutrophic lakes and the like.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims
It is intended that the present invention be construed as including the preferred embodiments and all such variations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A chlorophyll a concentration inversion method of a medium-low nutrient water body is characterized by comprising the following steps:
acquiring a plurality of earth surface reflectivity images;
acquiring reflectivity data of the medium and low nutrient water body according to the plurality of surface reflectivity images, and calculating a plurality of classification wave band reflectances according to the reflectivity data;
classifying the medium and low nutrient water body into various different water body types according to the reflectance of a plurality of classification wave bands;
selecting different inversion wave bands for various different water body types, calculating the reflection ratio of the inversion wave bands according to the different inversion wave bands, establishing a polynomial wave band ratio inversion model according to the reflection ratio of the inversion wave bands, and inverting the chlorophyll a concentrations of the various different water body types through the polynomial wave band ratio inversion model;
the polynomial band ratio inversion model is as follows:
Cchl-a＝a*BR(i) 2 +b*BR(i)+c
wherein Cchl-a is chlorophyll a concentration, BR (i) is the wave band reflectance of different water body types, and a, b and c are constants.
2. The chlorophyll a concentration inversion method of the medium-low nutrient water body as claimed in claim 1, wherein a plurality of earth surface reflectance images are obtained based on the sentinel No. 2 satellite, the sentinel No. 2 satellite carries a multispectral imager to obtain radiation measurements from 8 spectral bands from visible to near infrared, and the central wavelengths are 443nm, 490nm, 560nm, 665nm, 705nm, 740nm, 783nm and 842nm respectively.
3. The method for inverting the chlorophyll a concentration of the water body with the medium or low nutrition of claim 2, wherein before the reflectivity data of the water body with the medium or low nutrition is obtained, the atmospheric correction and nearest neighbor resampling pretreatment are carried out on the obtained multiple earth surface reflectivity images; the atmospheric correction is based on the libidtran radiative transfer model in the Sen2Cor processor, and the nearest neighbor resampling converts the spatial resolution of the spectral band from 20 meters to 10 meters.
4. The method for inverting the chlorophyll-a concentration of the water body with the medium or low nutrition of claim 1, wherein a surface feature spectrometer is used for obtaining the reflectivity data of the water body with the medium or low nutrition from a plurality of surface reflectivity images.
5. The method for inverting the chlorophyll-a concentration of a water body with low or medium nutrition content as claimed in claim 2, wherein said reflectance data comprises data of wavelength bands of 490nm, 560nm, 665nm, 705nm and 842nm.
6. The method for inverting the chlorophyll a concentration of the water body with the medium or low nutrition content as claimed in claim 5, wherein the water body with the medium or low nutrition content is classified into a plurality of different water body types according to a plurality of wave band reflection ratios, which includes the following cases:
when R490/R560 is not less than 0.8, the water body is type 1;
when R490/R560 is less than 0.8 and R665/R560 is not less than 0.6, the water body is type 2;
when R490/R560 is less than 0.8 and R665/R560 is less than 0.6, the water body is type 3,
wherein R is the reflectance.
7. The method for inverting chlorophyll a concentration of a water body with low or medium nutrition as claimed in claim 6, wherein said type 1 is a clear water body, type 2 is a phytoplankton dominant water body, and type 3 is a water body dominated by phytoplankton and other substances.
8. The chlorophyll a concentration inversion method of the medium and low nutrient water body as claimed in claim 7, wherein different inversion wave bands are selected for a plurality of different water body types, and the inversion wave band reflectance is calculated according to the different inversion wave bands, specifically comprising the following conditions:
the type 1 selects 665nm and 490nm, and the reflection ratio of the inversion wave band is R665/R490;
705nm and 560nm are selected as the type 2, and the reflection ratio of the inversion waveband is R705/R560;
the type 3 is selected from 842nm and 665nm, and the reflection ratio of the inversion waveband is R842/R665.
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| CN116952906A (en) * | 2023-09-20 | 2023-10-27 | 南京航天宏图信息技术有限公司 | Water body health state assessment method and device, electronic equipment and storage medium |
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| CN116952906A (en) * | 2023-09-20 | 2023-10-27 | 南京航天宏图信息技术有限公司 | Water body health state assessment method and device, electronic equipment and storage medium |
| CN116952906B (en) * | 2023-09-20 | 2024-01-12 | 南京航天宏图信息技术有限公司 | Water body health state assessment method and device, electronic equipment and storage medium |
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