CN104820224B - The MODIS satellite high-precision monitoring methods of nutrition-enriched water of lake chlorophyll a - Google Patents
The MODIS satellite high-precision monitoring methods of nutrition-enriched water of lake chlorophyll a Download PDFInfo
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- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 title claims abstract description 108
- 229930002868 chlorophyll a Natural products 0.000 title claims abstract description 107
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 86
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- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 abstract 1
- 229930002875 chlorophyll Natural products 0.000 abstract 1
- 235000019804 chlorophyll Nutrition 0.000 abstract 1
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- 241000192700 Cyanobacteria Species 0.000 description 1
- 235000002779 Morchella esculenta Nutrition 0.000 description 1
- 240000002769 Morchella esculenta Species 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
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Abstract
The present invention provides a kind of MODIS satellite high-precision monitoring methods of nutrition-enriched water of lake chlorophyll a, including：Screening changes chlorophyll a evaluation number that is sensitive and not influenceed by high suspended matter, Bao Yun, solar flare to chlorophyll-a concentration（BNDBI）；On the basis of bio-optical model simulation, the quantitative relationship between BNDBI and chlorophyll-a concentration is obtained；With reference to the water spectral information and corresponding water body chlorophyll alpha concentration of lake field monitoring in 2,013 2014 years, obtain and be based on situ measurements of hyperspectral reflectance（Rrs）With BNDBI Retrieving Chlorophyll-a Concentration algorithm；By simulating different aerosol types and thickness, different sun altitudes, moonscope angle and azimuth, ground monitoring remote sensing reflectance is obtained（Rrs）With R after the Rayleigh scattering correction of simulationrcBetween quantitative relationship；And then the Retrieving Chlorophyll-a Concentration algorithm based on situ measurements of hyperspectral reflectance data is extended to the satellite image data corrected by Rayleigh scattering.Based on this method, can accurately obtain eutrophic lake chlorophyll-a concentration year border, moon border changing rule and its spatial distribution.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to an MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water.
Background
The remote sensing technology is widely applied to monitoring water eutrophication and algal bloom, and the real-time monitoring capability of water quality is greatly improved (Matthews, 2010; Odermat et al, 2012). Chlorophyll a (Chla, mu g/L) is an important mark of algal bloom strength and water quality nutrition condition and becomes the most common parameter of water quality remote sensing (Chaeffer et al, 2012; Matthews, 2010; Qi et al, 2014). In recent years, due to the frequent occurrence of algal blooms, serious threats are generated to the quality of drinking water and irrigation water (Duan et al, 2009; Guo,2007), and the monitoring of water chlorophyll a by remote sensing is rapidly becoming a research hotspot (Paerl & Huisman, 2008; Paerl & Huisman, 2009; Matthews, 2010; Odermat et al, 2012). However, the water bodies that often have similar problems are generally the second class of water with complex optical properties (Morel & Prieur,1977), and therefore, performing accurate remote sensing inversion of water colors, particularly Chla, for inland and coastal waters with complex optical properties has been a serious challenge (IOCCG, 2000).
Four characteristics are commonly used to assess Chla concentration by a thorough review of literature on two classes of water: the absorption maximum of pigments at the 442nm band (Bricaud et al, 1995; Gitelson et al, 1992); the second absorption maximum of the pigment at the 665nm band (Bricaud et al, 1995); a reflection peak near the 572nm band due to absorption minima of pigments and scattering by SPM (Gitelson et al, 1992; Schalles et al, 1998); a reflection peak near the 700nm band due to the minimum total of phytoplankton, SPM, CDOM and pure water absorptions (Gitelson et al, 1992; Vasilolov & Kopelevich, 1982; Vos et al, 1986); and a fluorescence peak near the 685nm band (Gitelson et al, 1994; Gower, 1980; Gower et al, 1999). Based on the first property, the OC2, OC3 and OC4 algorithms were developed and can be applied to one type of water as well as to two types of water with low Chla concentrations (D' Sa & Miller, 2003; Horion et al, 2010; mlin et al, 2007; Witter et al, 2009) because all medium resolution ocean water spectrum analyzers can acquire the absorption band of the first feature of Chla (Matthews, 2010). However, the OC2-OC4 algorithm is only suitable for inverting the water body with complex optical characteristics, wherein the Chla concentration is less than 10 mu g/L, and cannot be applied to eutrophic water bodies. Some algorithm developments in recent years have attempted to use the second and third characteristics (green and red bands) and have made significant advances in improving the accuracy of Chla data results (Dall' Olmo & Gitelson, 2005; Gitelson et al, 2008; Le et al, 2013; Le et al, 2009; Shen et al, 2010; Tassan & Ferrari, 2003; Thiemann & Kaufmann, 2000). However, with the exception of the Shen and Le studies, most of these studies are based on field-measured spectral data (Le et al, 2013; Shen et al, 2010). Currently, algorithms based on band ratio values around 700nm and around 670nm are widely used to estimate Chla concentration in eutrophic water (Duan et al, 2007; Gitelson et al, 1993; Gons, 1999; Moses & Gitlson, 2009). The correlation between Chla and 700/670nm ratios is mainly derived from the fourth property of chlorophyll a spectra. Later, this ratio algorithm evolved and developed into a "tri-band algorithm" (Dall' Olmo & Gitelson, 2005; Duan et al, 2010; Gitelson et al, 2008; Moses et al, 2009; Zimba & Gitelson,2006) and a "quad-band algorithm" that greatly improved the accuracy of the inversion of Chla concentrations in turbid water (Le et al, 2013; Le et al, 2009). FLH (Dierberg & Carrizer, 1994; Giardito et al, 2005), MCI (Binding et al, 2011; Gower et al, 2005) and MPH (Matthews et al, 2012) are linear baseline algorithms based on fluorescence maxima around the 685nm band, FLH being suitable for bodies of water with concentrations less than 30 μ g/L and the other two for bodies of water with concentrations less than 100 μ g/L (Gower et al, 2005; Matthews et al, 2012). In addition to the above-mentioned Chla algorithm, artificial neural network algorithms (Keiner,1999), multiple regression analysis (Tyler et al, 2006), EOF (Craig et al, 2005; Qiet al, 2014), SDA (Oyama et al, 2010; Oyama et al, 2009), and LUT methods (Yang et al, 2011) are also applied to estimation of Chla. Although these algorithms improve partial correlation over other algorithms, these improvements are negligible to their complex mathematical processes (Matthews, 2010).
Typically, the visible and near infrared (400-1000 nm) portions of the spectrum, measured by remote sensing instruments, are commonly used to monitor bodies of water. Therefore, the typical ocean water color sensors SeaWiFS, MODIS and MERIS are more suitable for monitoring water color parameters. From the literature of Chla telemetry inversion, SeaWiFS and MODIS can be used for OC algorithm and low Chla concentration, and MERIS can be used for high concentration Chla inversion through the ratio of red light to near infrared band, MCI algorithm, NN algorithm, etc. (odiermat et al, 2012). MERIS has obvious advantages over other sensors in measuring Chla of turbid eutrophic paste water body, but the measuring period of 16 days is not satisfactory. In contrast, MODIS has a high temporal resolution of 1-2 days and better spatial resolution than SeaWiFS (250/500 m for MODIS and 1000m for SeaWiFS), and is more suitable for real-time water color parameter monitoring. Unfortunately, there are several problems that remain unresolved with MODIS for real-time Chla monitoring. Firstly, the current Chla algorithm of MODIS (such as OC2, OC3, OC4 and OCI, etc.) is not suitable for eutrophic water body with high turbidity; secondly, the ocean water color wave band used by MODIS for water body research is saturated in coastal water areas and inland water areas, and almost no data can be used, so that Chla can be inverted only by using an unsaturated land broadband algorithm; finally, at present, there is no reliable atmospheric correction method to help MODIS acquire Rrs data (data subjected to completely accurate atmospheric correction) of inland turbid lakes. Therefore, a new method is developed by utilizing the unsaturated spectrum band of MODIS through local atmospheric correction to realize the inversion of Chla in the highly turbid eutrophic water body, and the method becomes the direction of the next research.
Disclosure of Invention
The invention aims to provide an MODIS satellite high-precision monitoring method for chlorophyll a in a water body of an eutrophic lake, which can accurately obtain the time-space distribution of the chlorophyll a concentration of the shallow lake, accurately analyze the occurrence, development and trend of cyanobacterial bloom, scientifically evaluate the treatment and ecological restoration effects of lake pollution, and provide scientific support for scientific decisions of water resource management and water environment protection of water conservancy, environmental protection and other departments.
The above object of the invention is achieved by the features of the independent claims, the dependent claims developing the features of the independent claims in alternative or advantageous ways.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water body comprises the following steps:
1) screening chlorophyll a evaluation index BNDBI which is sensitive to the change of the concentration of chlorophyll a and is not influenced by high suspended matters, cloudiness and solar flare;
the chlorophyll a evaluation index BNDBI which is sensitive to the chlorophyll a concentration change and is not influenced by high suspended matter, thin cloud and solar flare is characterized in that a difference value expression form between red and green wave bands is selected by taking blue light and near infrared wave bands as baselines based on the chlorophyll a, inorganic suspended matter and yellow matter spectral response characteristics, so that the adverse influence of the high suspended matter, thin cloud and solar flare on the chlorophyll a concentration estimation can be avoided, and the algae index is used as a chlorophyll a concentration remote sensing monitoring index;
2) based on the simulation of a biological optical model, the quantitative relation between BNDBI and chlorophyll a concentration is determined;
on the basis of a biological optical model, numerical simulation under different scenes is carried out by combining actually measured data of lakes, the quantitative relation between BNDBI and chlorophyll a concentration is determined, and meanwhile, the insensitivity of the index to a high-turbidity water body is theoretically determined;
3) obtaining ground monitoring remote sensing reflectance ratio RrsAnd simulated Rayleigh scattering corrected RrcA quantitative relationship between;
remote sensing reflectance R based on ground monitoring under the conditions of different aerosol types, thicknesses, different solar heights, different satellite observation angles and different azimuth angles in simulated lake areasrsBNDBI and R based on simulated Rayleigh scattering correctionrcThe quantitative relationship between BNDBI of (a);
4) obtaining an inversion algorithm of chlorophyll a concentration based on an MODIS satellite image;
based on the steps and the method, a chlorophyll a inversion algorithm based on ground actual measurement spectral data is applied to satellite image data subjected to Rayleigh scattering correction, and the annual and lunar change rule and the spatial distribution of the chlorophyll a concentration of the eutrophic lake are obtained after a plurality of time-series satellite images are processed based on the inversion algorithm.
As a further improvement of the invention, in the step 1), the spectral response characteristics of chlorophyll a, inorganic suspended matters and yellow substances are obtained from measured spectral data R in the field of lakesrsThe adopted monitoring instrument is a dual-channel ground spectrum monitor of American ASD company.
As a further improvement of the present invention, in the step 2), the expression form of chlorophyll a evaluation index BNDBI based on the ground measured spectrum data is as follows:
BNDBI＝(Rrs555'-Rrs645')/(Rrs555'-Rrs645')
as a further improvement of the present invention, the step 2) "performing numerical simulation of different scenarios" specifically includes:
firstly, under the condition that the concentration of inorganic suspended matters and yellow substances are kept unchanged, obtaining the quantitative relation between BNDBI and the concentration of chlorophyll a;
secondly, simulating the response of the BNDBI to the concentration of the inorganic suspended matters when the concentrations of the chlorophyll a and the yellow substances are not changed;
and finally simulating the influence of the concentration change of the yellow substance on BNDBI when the concentrations of the chlorophyll a and the inorganic suspended matters are kept unchanged.
As a further improvement of the invention, in the step 3), the aerosol type refers to the result of LUT of SeaDas, the aerosol thickness refers to the perennial monitoring result range of the lake area, and the satellite observation angle is determined according to the relative positions of the sun, the satellite and the lake.
As a further improvement of the present invention, in the step 4), the expression form of the BNDBI index applied to the MODIS image is:
and the BNDBI index of the MODIS image is established on the basis of radiometric calibration, geometric correction and atmospheric Rayleigh scattering correction of the MODIS satellite image.
According to the technical scheme, the MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water body determines the chlorophyll a monitoring index BNDBI insensitive to inorganic suspended matters and yellow substances in the water body based on the lake chlorophyll a remote sensing monitoring basic principle, and realizes high-precision estimation of chlorophyll a in MODIS images through the core of the quantitative relation between the chlorophyll a concentration and BNDBI of ground actual measurement spectrum and BNDBI data after Rayleigh scattering correction, so that the space-time distribution of the chlorophyll a in lakes is reflected more objectively and truly. The high-precision monitoring of the chlorophyll a can effectively realize the lake algal bloom risk and the effective evaluation of a water source area; the long-term high-precision monitoring of the chlorophyll a concentration of the lake is beneficial to scientific evaluation of the change and the development trend of the chlorophyll a between the years, effective evaluation of the performance of lake pollution treatment and ecological restoration, and scientific support for scientific decisions of water resource management and water environment protection of water conservancy, environmental protection and other departments.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
Drawings
The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of the basic principle of monitoring chlorophyll a by BNDBI index.
FIG. 2 is a quantitative relationship between BNDBI and chlorophyll a concentration under theoretical simulation.
Fig. 3 is a quantitative relationship between Rrs-based BNDBI and Rrc-based BNDBI for different aerosol types and thicknesses, different solar altitude, satellite observation angle, and azimuth.
Fig. 4 shows the results of high-precision monitoring of the spatial distribution of MODIS satellites for nested lake chlorophyll a (9/25/2012).
Fig. 5 is a schematic diagram of the application of the BNDBI algorithm to high aerosol impact.
Fig. 6 is a schematic diagram of the application of the BNDBI algorithm to thin cloud effects.
Fig. 7 is a schematic diagram of the application of the BNDBI algorithm to solar flare impact.
In the above-mentioned diagrams 1 to 7, the coordinates, marks or other representations expressed in english are all known in the art and are not described in detail in this embodiment.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, and that the concepts and embodiments disclosed herein are not limited to any embodiment. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
The MODIS satellite data is used for carrying out high-precision monitoring on the chlorophyll a concentration of the eutrophic lake, and the purpose is realized as follows:
screening chlorophyll a evaluation index (BNDBI) which is sensitive to chlorophyll a concentration change and is not influenced by high suspended matters, thin clouds and solar flares;
theoretically demonstrating the quantitative relation between BNDBI and chlorophyll a concentration on the basis of biological optical model simulation;
combining the spectrum information of the water body monitored in the field of the nested lake in the years of 2013-2014 and the corresponding concentration of chlorophyll a in the water body to obtain a chlorophyll a inversion algorithm based on the ground actual measurement spectrum and BNDBI;
by simulating different aerosol types and thicknesses, different solar altitude angles, satellite observation angles and azimuth angles, R-based acquisition is obtainedrsBNDBI and based on RrcThe quantitative relationship between BNDBIs of (a); and then, the chlorophyll a inversion algorithm based on the ground actual measurement spectral data is popularized to the satellite image data subjected to Rayleigh scattering correction, and the chlorophyll a concentration and the spatial distribution of the lake whole water area are estimated.
The method of carrying out the foregoing method will now be described in detail, by way of example only, with reference to the accompanying drawings
Step 1, determining monitoring evaluation index BNDBI of chlorophyll a
The chlorophyll a evaluation index BNDBI which is sensitive to the change of the concentration of chlorophyll a and is not influenced by high suspended matter, thin cloud and solar flare is that based on the chlorophyll a and the spectral response characteristics of suspended matter, a standard difference form of red and green wave bands is selected by taking a blue light wave band and a near infrared wave band as baselines, the adverse influence of the high suspended matter, the thin cloud and the solar flare on the estimation of the concentration of the chlorophyll a can be avoided, and the algae index is used as a chlorophyll a concentration remote sensing monitoring index.
Specifically, based on the basic monitoring principle of optical active substances (chlorophyll a, inorganic suspended matters and yellow substances) in the water body, the spectral characteristics of the three optical active substances in the water body are researched, the advantages and the disadvantages of the existing chlorophyll a in two types of water bodies are combined, the monitoring indexes which are not influenced by the inorganic suspended matters and the yellow substances in the water body are selected to be used as the basic indexes for monitoring the cyanobacteria bloom MODIS satellite while the concentration of the chlorophyll a is accurately estimated, and the adverse conditions of other optical active substances in the water body on monitoring the concentration of the chlorophyll a are overcome.
In this embodiment, since the water body with high chlorophyll a has a reflection peak in the green light band (570nm), and the chlorophyll a strongly absorbs at 665nm to cause a reflection valley in the red light band (fig. 1), the content of chlorophyll a can be estimated from the chlorophyll a characteristic band corresponding to MODIS. Fig. 1 shows the spectrums of the high chlorophyll a, the high turbidity and the general water body and the difference between the three under the condition of the MODIS waveband setting, and it can be seen that if the wavebands of 555nm and 645nm are taken as base points at two ends, the high chlorophyll a water body and the high turbidity water body have the largest difference, and in addition, based on the blue light waveband (469nm) and the near infrared waveband (859nm) as baselines, the influence caused by certain thin clouds and solar flares can be filtered. According to the characteristics, BNDBI (Baseline normalizedfluorescence index) index is provided:
BNDBI＝(Rrs555'-Rrs645')/(Rrs555'-Rrs645')
wherein R isrsAnd (lambda) is the remote sensing reflectance of the water body at the lambda wavelength obtained by ground measurement.
Step 2, the quantitative relation between BNDBI and chlorophyll a concentration is proved by simulation of a biological optical model
On the basis of a biological optical model, numerical simulation under different scenes is carried out by combining with actually measured data of the nested lake, and the quantitative relation between BNDBI and chlorophyll a concentration and the influence of other optical active substances in the water body on the algorithm are theoretically proved.
In the embodiment, for a common water body, the remote sensing reflectance of the water body is in direct proportion to the inherent optical property of the water body,
a(λ)＝aw(λ)+aph(λ)+ad(λ)+ag(λ)
bb(λ)＝bbw(λ)+bbp(λ) (2)
wherein a iswAnd bbwCorresponding to the absorption coefficient and the back scattering coefficient of pure water; and a isph、adAnd agThe absorption coefficients of phytoplankton pigment, inorganic suspended matters and yellow substances are closely related to the amount of corresponding substances in the water body, bbpIs the backscattering coefficient of particles in the water body, and the coefficient has close relation with inorganic suspended matters in the water body with low algae content. Wherein,
according to formula (1), the BNDBI and the chlorophyll a concentration have the following relationship,
according to equation (4), there is a monotonic relationship between BNDBI and chlorophyll a concentration, i.e. BNDBI increases with increasing chlorophyll a concentration. Therefore, assuming that the concentration of inorganic suspended matters in the water body is 50mg/L, the quantitative relationship between the BNDBI and the chlorophyll a concentration based on the simulation of the biological optical model is shown in FIG. 2 under the condition that the influence of yellow substances is neglected.
According to the actually measured spectrum data and the corresponding chlorophyll a concentration data of the field of the nested lake in 2013-2014, an inversion algorithm of the nested lake chlorophyll a based on the actually measured spectrum data Rrs is constructed.
Chla＝982.3*BNDBI4+71.86*BNDBI3+562.4*BNDBI2+79.05*BNDBI+6.6(5)
Step 3, acquiring spectral data R based on ground actual measurementrsBNDBI and simulated Rayleigh scattering corrected RrcThe quantitative relationship between BNDBI of
Examine the area of the nido lakeGround-based actually-measured spectral data R of different aerosol types and thicknesses, different solar altitude angles, satellite observation angles and azimuth anglesrsBNDBI and simulated Rayleigh scattering corrected RrcThe influence of the quantitative relationship between the BNDBIs of (a) and the quantitative model between the two is determined by modeling data.
In this embodiment, the inversion algorithm for obtaining chlorophyll a based on the actually measured spectrum data is to be generalized to the satellite image data, and atmospheric correction is not negligible. However, an effective accurate atmosphere correction algorithm for a high-turbidity water body is still lacking at present, and the rayleigh scattering correction of the MODIS image is adopted at this time, that is, through the correction, the optical information of the top of the atmosphere layer removes the influence of the rayleigh scattering, and still contains aerosol information and ground information. Based on the rayleigh scatter corrected data, BNDBI is expressed as:
wherein R isrc(λ) is the reflectance at the λ wavelength with rayleigh correction. RrcThe MODIS data was Rayleigh-scattering corrected and then converted to Rayleigh-scattering corrected reflectivities based on Hu et al (2004):
in the formula,is to correct ozone andsensor radiance after absorption effect of other gases, F0Is the solar irradiance outside the atmosphere, theta, when data is acquired0Is the zenith angle of the sun, RrIs the Reyle reflectance predicted using 6S (Vermote et al, 1997).
Based on radiative transfer theory and assuming an uncoupled marine-atmospheric system, RrcCan be expressed as:
Rrc＝Ra+t0tRtarget(8)
in the formula, RaIs the aerosol reflectance (including interactions from aerosol molecules), RtargetIs the surface reflectivity, t, of the field measured target (algae or water)0Is the atmospheric transmission from the sun to the target, and t is the atmospheric transmission from the target to the satellite sensor. The floating algae usually takes the form of floating oil on the water surface due to the influence of wind and water flow, and therefore t can be regarded as the light transmittance of the floating algae.
In order to investigate the influence caused by different aerosol types and thicknesses and satellite observation, the ground-based actually-measured spectral data R is subjected to measurement according to different aerosol types and thicknesses, different solar altitude angles, satellite observation angles and azimuth angles in the nido lake regionrsBNDBI and simulated Rayleigh scattering corrected RrcThe influence of the quantitative relationship between the BNDBIs (fig. 3), and a quantitative model between the two was determined by modeling the data,
BNDBI(Rrc)＝1.051BNDBI(Rrs)-0.007
(9)
step 4, obtaining an inversion algorithm of chlorophyll a concentration based on MODIS satellite images
Based on the steps and the method, a chlorophyll a inversion algorithm based on ground actual measurement spectral data is applied to satellite image data subjected to Rayleigh scattering correction, and the annual and lunar change rule and the spatial distribution of the chlorophyll a concentration of the eutrophic lake are obtained after a plurality of time-series satellite images are processed based on the inversion algorithm.
Based on the formula (5) and the formula (9), a MODIS satellite high-precision inversion model of the green lake chlorophyll a can be obtained.
The method comprises the following specific processes of ① performing geometric correction and radiometric calibration calculation on an obtained MODIS image, performing correction by using GeogrAN _ SNhic Lat/Lon projection in combination with longitude and latitude information in 1B data, wherein the corrected position accuracy reaches 0.5 pixel, extracting a lake water area by using a lake vector boundary in the ERDAS through a mask technology, removing the influence of island vegetation, resampling MODIS 500m image data to 250m by using a nearest neighbor method, and calculating R of the MODIS image in band 1(645nm), band 2(859nm), band 3(469nm) and band 4(555nm) in a pixel-by-pixel manner according to ② MODIS imagerc③ calculating BNDBI value one by one according to formula (6), ④ then obtaining the calculated chlorophyll a space distribution result according to formula (5) and formula (9) (see figure 4).
In addition, the baseline algorithm is subjected to chlorophyll a concentration inversion comparison under the conditions of high turbid water, thin cloud and solar flare, and the results are respectively shown in fig. 5-7.
By the method, the algal bloom area of the algal bloom mixed pixel in a certain MODIS image can be estimated, and the high-precision estimation and the space-time distribution of the lake algal bloom area can be objectively and truly reflected. The high-precision monitoring of the area of the blue algae can effectively realize the lake algal bloom risk and the effective evaluation of a water source area; in addition, after the MODIS historical images are calculated one by one through the method, long-term high-precision monitoring of the lake blue algae area can be achieved, scientific assessment of the change of the actual strength of the algal bloom and the development trend of the algal bloom between the years is facilitated, the performance of lake pollution treatment and ecological restoration is effectively assessed, and scientific support is provided for scientific decisions of water resource management and water environment protection of water conservancy, environmental protection and other departments.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (6)
1. A MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water body is characterized by comprising the following steps:
1) screening chlorophyll a evaluation index BNDBI which is sensitive to the change of the concentration of chlorophyll a and is not influenced by high suspended matters, cloudiness and solar flare;
the chlorophyll a evaluation index BNDBI which is sensitive to the chlorophyll a concentration change and is not influenced by high suspended matter, thin cloud and solar flare is characterized in that a difference value expression form between red and green wave bands is selected by taking blue light and near infrared wave bands as baselines based on the chlorophyll a, inorganic suspended matter and yellow matter spectral response characteristics, so that the adverse influence of the high suspended matter, thin cloud and solar flare on the chlorophyll a concentration estimation can be avoided, and the evaluation index is used as a chlorophyll a concentration remote sensing monitoring index;
2) based on the simulation of a biological optical model, the quantitative relation between BNDBI and chlorophyll a concentration is determined;
on the basis of a biological optical model, numerical simulation under different scenes is carried out by combining actually measured data of lakes, the quantitative relation between BNDBI and chlorophyll a concentration is determined, and meanwhile, the insensitivity of the index to a high-turbidity water body is theoretically determined;
3) obtaining ground monitoring remote sensing reflectance ratio RrsAnd simulated Rayleigh scattering corrected RrcA quantitative relationship between;
remote sensing reflectance R based on ground monitoring under the conditions of different aerosol types, thicknesses, different solar heights, different satellite observation angles and different azimuth angles in simulated lake areasrsBNDBI and R based on simulated Rayleigh scattering correctionrcThe quantitative relationship between BNDBI of (a);
4) obtaining an inversion algorithm of chlorophyll a concentration based on an MODIS satellite image;
based on the steps and the method, a chlorophyll a inversion algorithm based on ground actual measurement spectral data is applied to satellite image data subjected to Rayleigh scattering correction, and an annual and lunar change rule and spatial distribution of the chlorophyll a concentration of the eutrophic lake are obtained after a plurality of time-series satellite images are processed based on the inversion algorithm.
2. The MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water according to claim 1, wherein in the step 1), the spectral response characteristics of chlorophyll a, inorganic suspended matters and yellow substances are derived from the measured spectral data R in the field of lakesrsThe adopted monitoring instrument is a dual-channel ground spectrum monitor of American ASD company.
3. The MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water according to claim 1, wherein in the step 2), the chlorophyll a evaluation index BNDBI expression form based on the ground measured spectrum data is as follows:
<mrow> <mtable> <mtr> <mtd> <mrow> <mi>B</mi> <mi>N</mi> <mi>D</mi> <mi>B</mi> <mi>I</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>)</mo> </mrow> <mo>/</mo> <mrow> <mo>(</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>=</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>555</mn> <mo>-</mo> <mo>&lsqb;</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>469</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>555</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>+</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>859</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>555</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>R</mi> <mi>r</mi> <mi>s</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>=</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>645</mn> <mo>-</mo> <mo>&lsqb;</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>469</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>645</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>+</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>859</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>645</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>.</mo> </mrow>
4. the MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water according to claim 1, wherein the "performing numerical simulation under different situations" in step 2) specifically comprises:
firstly, under the condition that the concentration of inorganic suspended matters and yellow substances are kept unchanged, obtaining the quantitative relation between BNDBI and the concentration of chlorophyll a;
secondly, simulating the response of the BNDBI to the concentration of the inorganic suspended matters when the concentrations of the chlorophyll a and the yellow substances are not changed;
and finally simulating the influence of the concentration change of the yellow substance on BNDBI when the concentrations of the chlorophyll a and the inorganic suspended matters are kept unchanged.
5. The MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water according to claim 1, wherein in step 3), the aerosol type refers to the LUT result of SeaDas, the aerosol thickness refers to the perennial monitoring result range of lake areas, and the satellite observation angle is determined according to the relative positions of the sun, the satellite and the lake.
6. The MODIS satellite high-precision monitoring method for chlorophyll a in eutrophic lake water according to claim 1, wherein in the step 4), the BNDBI index expression form applied to MODIS images is as follows:
<mrow> <mi>B</mi> <mi>N</mi> <mi>D</mi> <mi>B</mi> <mi>I</mi> <mo>=</mo> <mfrac> <mrow> <mo>(</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>)</mo> </mrow> </mfrac> <mo>;</mo> </mrow>
<mrow> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>555</mn> <mo>&prime;</mo> </msup> <mo>=</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <mn>555</mn> <mo>-</mo> <mo>&lsqb;</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>469</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>555</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>+</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>859</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>555</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>;</mo> </mrow>
<mrow> <mi>R</mi> <mi>r</mi> <mi>c</mi> <msup> <mn>645</mn> <mo>&prime;</mo> </msup> <mo>=</mo> <mi>R</mi> <mi>r</mi> <mi>c</mi> <mn>645</mn> <mo>-</mo> <mo>&lsqb;</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>469</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>645</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>+</mo> <mi>R</mi> <mi>r</mi> <mi>s</mi> <mn>859</mn> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <mn>645</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>859</mn> <mo>-</mo> <mn>469</mn> <mo>)</mo> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>;</mo> </mrow>
and the BNDBI index of the MODIS image is established on the basis of radiometric calibration, geometric correction and atmospheric Rayleigh scattering correction of the MODIS satellite image.
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