CN104820224A - MODIS satellite high-precision monitoring method for chlorophyll-a in eutrophic lake water body - Google Patents

Abstract

The invention provides an MODIS satellite high-precision monitoring method for chlorophyll-a in a eutrophic lake water body. The method comprises the following steps: screening a chlorophyll-a evaluation index (BNDBI) which is sensitive to the concentration change of chlorophyll-a and not affected by highly suspended matters, thin cloud and solar flares; acquiring the quantitative relationship between BNDBI and the concentration of chlorophyll-a on the basis of biological optical model simulation; acquiring a chlorophyll-a inversion algorithm based on ground measured spectrum (Rrs) and BNDBI by referring to water body spectral information and corresponding water body chlorophyll-a concentration of 2013-2014 lake field monitoring; acquiring the quantitative relationship between the ground monitoring and remote sensing reflectance ratio (Rrs) and Rrc after simulated Rayleigh scattering correction by simulating different aerosol types and thicknesses and different solar elevation angles, satellite observation angles and azimuth angles; and extending the chlorophyll-a inversion algorithm based on ground measured spectrum data to satellite image data after Rayleigh scattering correction. Based on the method, inter-annual and inter-monthly change rules and spatial distribution of chlorophyll-a in a eutrophic lake can be acquired accurately.

Description

The MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a

Technical field

The present invention relates to remote sensing technology field, particularly relate to a kind of MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a.

Background technology

Remote sensing technology is widely used in monitoring body eutrophication and algal tufa, substantially improves Real-Time Monitoring ability (Matthews, 2010 of water quality; Odermatt et al., 2012).Chlorophyll a (Chla, μ g/L) is the important symbol of algal tufa intensity and water quality nutrition condition, becomes the parameter that water quality remote sensing is the most general, (Chaeffer et al., 2012; Matthews, 2010; Qi et al., 2014).In recent years, due to the algal tufa frequently occurred, serious threat (Duan et al., 2009 are created to potable water and irrigation water water quality; Guo, 2007), utilize remote sensing monitoring water body chlorophyll alpha to become rapidly study hotspot (Paerl & Huisman, 2008; Paerl & Huisman, 2009; Matthews, 2010; Odermatt et al., 2012).But, often occur that the water body of Similar Problems is all two class water (the Morel & Prieur of optical characteristics complexity usually, 1977), therefore, carry out Ocean Color Remote Sensing inverting accurately (particularly Chla) for the inland of optical characteristics complexity and coastal waters is a stern challenge (IOCCG, 2000) always.

By the document of comprehensive reviewing about case Ⅱ waters, four characteristics are usually used to assessment Chla concentration: pigment absorbs maximal value (Bricaud et al., 1995 at 442nm wave band place; Gitelson et al., 1992); Pigment absorbs maximal value (Bricaud et al., 1995) at 665nm wave band place second; The reflection peak (Gitelson et al., 1992 that produce due to the scattering of pigment absorption minimum value and SPM near 572nm wave band; Schalles etal., 1998); And the reflection peak (Gitelson et al., 1992 that are formed minimum due to the total value of phytoplankton, SPM, CDOM and absorbing pure water near 700nm wave band; Vasilkov & Kopelevich, 1982; Vos et al., 1986); And fluorescence peak (Gitelson et al., 1994 near 685nm wave band; Gower, 1980; Goweret al., 1999).Based on the first characteristic, OC2, OC3 and OC4 algorithm is developed and can be applied to two class water (D'Sa & Miller, 2003 of a class water and low Chla concentration; Horion et al., 2010; M é lin etal., 2007; Witter et al., 2009), because all intermediate resolution ocean color spectroanalysis instruments can obtain the absorption bands (Matthews, 2010) of the fisrt feature of Chla.But OC2-OC4 algorithm is only applicable to the optical characteristics Complex water body that inverting Chla concentration is less than 10 μ g/L, eutrophication water cannot be applied to.Some algorithm development attempts employing second and the 3rd characteristic (green glow and red spectral band) in recent years, and achieves great progress (Dall'Olmo & Gitelson, 2005 improving in Chla data result accuracy; Gitelson et al., 2008; Le et al., 2013; Le et al., 2009; Shen et al., 2010; Tassan & Ferrari, 2003; Thiemann & Kaufmann, 2000).But except the research of shen and le, most of this respect research is all spectroscopic data (Le et al., 2013 based on field survey; Shen et al., 2010).At present, the band ratio algorithm based on about 700nm and about 670nm is widely used in Chla concentration (Duan et al., 2007 of assessing eutrophication water; Gitelson et al., 1993; Gons, 1999; Moses & Gitlson, 2009).Correlativity between Chla and 700/670nm ratio is mainly derived from the 4th characteristic of chlorophyll a spectrum.Afterwards, this developed again and developed into " three wave band algorithms " (Dall'Olmo & Gitelson, 2005 than value-based algorithm; Duan et al., 2010; Gitelson et al., 2008; Moses et al., 2009; Zimba & Gitelson, 2006) and " four wave band algorithms ", these algorithms substantially increase inversion accuracy (Le et al., 2013 of Chla concentration in feculent water body; Le et al., 2009).FLH (Dierberg & Carriker, 1994; Giardinoet al., 2005), MCI (Binding et al., 2011; Gower et al., 2005) and MPH (Matthewset al., 2012) be then based on 685nm wave band near the linear baseline algorithm of fluorescence maximal value, FLH is applicable to the water body that concentration is less than 30 μ g/L, other two water body (Goweret al., 2005 being applicable to concentration and being less than 100 μ g/L; Matthews et al., 2012).Except Chla algorithm above-mentioned, artificial neural network algorithm (Keiner, 1999), multiple regression analysis (Tyler et al., 2006), EOF (Craig et al., 2005; Qi et al., 2014), SDA (Oyama et al., 2010; Oyama et al., 2009), and LUT method (Yang et al., 2011) is also applied to the estimation of Chla.Although these algorithms are part correlation property than other algorithms to improve, these improve and then seem insignificant (Matthews, 2010) with the mathematical procedure of its complexity.

Usually, the visible ray measured by remote sensing instrument and near infrared spectrum (400-1000nm) part, be normally used for monitoring water body.Therefore, typical ocean color sensor SeaWiFS, MODIS and MERIS are more suitable for monitoring water colour parameter.From the document of Chla remote-sensing inversion, SeaWiFS and MODIS can be used for OC algorithm and low Chla concentration, MERIS can be used for high concentration Chla inverting (Odermatt et al., 2012) by ruddiness and near-infrared band ratio, MCI algorithm and NN algorithm etc.MERIS has obvious advantage than other sensors in the Chla measuring muddy eutrophy paste water body, but its measuring period of 16 days is barely satisfactory.By contrast, MODIS has the high time resolution of 1-2 days and spatial resolution (MODIS be 250/500m, SeaWiFS be 1000m) more better than SeaWiFS, is more applicable to real-time water colour parameter monitoring.Regrettably, be used for real-time Chla monitoring with MODIS and also there are some problems not yet needing to solve.First, the Chla algorithm (as OC2, OC3, OC4 and OCI etc.) of existing MODIS is not suitable for high muddy eutrophication water; The ocean color wave band that the second, MODIS is used for water body research is all saturated in coastal waters and inland waters, does not almost have data to use, therefore, the broadband algorithm in unsaturated land can only be utilized to carry out inverting Chla; Finally, not have at present atmospheric correction way comparatively reliably to help MODIS to get the Rrs data (data through atmospheric correction accurately completely) in muddiness lake, inland.Therefore, utilize the unsaturated spectral band that MODIS corrects through local atmosphere, develop a new method, realize the inverting of Chla in high muddy eutrophication water, become the direction of next step research.

Summary of the invention

The object of the present invention is to provide a kind of MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a; accurately can obtain shallow lake chlorophyll-a concentration spatial and temporal distributions; the generation of accurate analysis blue-green alga bloom, state of development and trend; Scientific evaluation lake pollution is administered and Effect of Ecological Restoration, for the water resources management of the department such as water conservancy, environmental protection, the science decision of water environment protection provide science and technology support.

Above-mentioned purpose of the present invention is realized by the technical characteristic of independent claims, and dependent claims develops the technical characteristic of independent claims with alternative or favourable mode.

For reaching above-mentioned purpose, the technical solution adopted in the present invention is as follows:

A MODIS satellite high-precision monitoring method for nutrition-enriched water of lake chlorophyll a, said method comprising the steps of:

1) screening is on chlorophyll-a concentration sensitive and don't the chlorophyll a evaluation number BNDBI that affects by high suspended matter, Bao Yun, solar flare;

Wherein, described on chlorophyll-a concentration sensitive and don't refer to based on chlorophyll a, mineral suspensions and yellow substance spectral response characteristics by the chlorophyll a evaluation number BNDBI that high suspended matter, Bao Yun, solar flare affect, with blue light and near-infrared band for baseline, select the difference expression-form between red, green wave band, the adverse effect that high suspended matter, Bao Yun, solar flare are estimated chlorophyll-a concentration can be avoided, and using this algae index as chlorophyll-a concentration remote sensing monitoring index;

2) based on bio-optical model simulation, the quantitative relationship between BNDBI and chlorophyll-a concentration is specified;

On the basis of bio-optical model, in conjunction with the measured data in lake, carry out the numerical simulation under different sight, determine the quantitative relationship of BNDBI and chlorophyll-a concentration, determine this exponent pair height feculent water body insensitivity theoretically simultaneously;

3) ground monitoring remote sensing reflectance R is obtained _rsr after correcting with the Rayleigh scattering of simulation _rcbetween quantitative relationship;

Simulate lakeland under different aerosol type and thickness, different sun altitude, moonscope angle and position angle situation, based on the remote sensing reflectance R of ground monitoring _rsbNDBI and the R to correct based on the Rayleigh scattering simulated _rcbNDBI between quantitative relationship;

4) the chlorophyll-a concentration inversion algorithm based on MODIS satellite image is obtained;

Based on abovementioned steps and method, by the Retrieving Chlorophyll-a Concentration algorithm application based on situ measurements of hyperspectral reflectance data to the satellite image data of correcting through Rayleigh scattering, based on aforesaid inversion algorithm method, obtain after to several seasonal effect in time series satellite image process the year border of eutrophic lake chlorophyll-a concentration, the moon border Changing Pattern and space distribution thereof.

As a further improvement on the present invention, described step 1) in, chlorophyll a, mineral suspensions and yellow substance spectral response characteristics come from the spectroscopic data R of lake fieldwork _rs, the monitoring instrument of employing is the binary channels ground light spectrum monitoring instrument of ASD company of the U.S..

As a further improvement on the present invention, described step 2) in, the chlorophyll a evaluation number BNDBI expression-form based on situ measurements of hyperspectral reflectance data is:

BNDBI＝(Rrs555'-Rrs645')/(Rrs555'-Rrs645')

$\mathrm{Rrs}{555}^{\′}=\mathrm{Rrs}555-[\mathrm{Rrs}469\×\frac{(859-555)}{(859-469)}+\mathrm{Rrs}859\×\frac{(555-469)}{(859-469)}].$

$\mathrm{Rrs}{645}^{\′}=\mathrm{Rrs}645-[\mathrm{Rrs}469\×\frac{(859-645)}{(859-469)}+\mathrm{Rrs}859\×\frac{(645-469)}{(859-469)}]$

As a further improvement on the present invention, described step 2) in " carrying out the numerical simulation of different sight " specifically comprise:

First, when mineral suspensions concentration and yellow substance remain unchanged, the quantitative relationship between BNDBI and chlorophyll-a concentration is obtained;

Secondly, simulation chlorophyll a and yellow substance concentration constant time, BNDBI is to the response of mineral suspensions concentration;

When finally simulation chlorophyll a and mineral suspensions concentration remain unchanged, yellow substance concentration change is on the impact of BNDBI.

As a further improvement on the present invention, described step 3) in, aerosol type is with reference to the result of the LUT of SeaDas, and gasoloid thickness is with reference to the long-term monitoring result scope of lakeland, and moonscope angle is then determined according to the relative position in the sun, satellite and lake.

As a further improvement on the present invention, described step 4) in, the BNDBI index expression-form being applied to MODIS image is:

$\mathrm{BNDBI}=\frac{\mathrm{Rrc}{555}^{\′}-\mathrm{Rrc}{645}^{\′}}{\mathrm{Rrc}{555}^{\′}+\mathrm{Rrc}{645}^{\′}};$

$\mathrm{Rrc}{555}^{\′}=\mathrm{Rrc}555-[\mathrm{Rrs}469\×\frac{(859-555)}{(859-469)}+\mathrm{Rrs}859\×\frac{(555-469)}{(859-469)}];$

$\mathrm{Rrc}{645}^{\′}=\mathrm{Rrc}645-[\mathrm{Rrs}469\×\frac{(859-645)}{(859-469)}+\mathrm{Rrs}859\×\frac{(645-469)}{(859-469)}];$

Further, the BNDBI index of MODIS image is based upon on the basis that the radiation calibration of MODIS satellite image, geometric correction and air Rayleigh scattering correct.

From the above technical solution of the present invention shows that, the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a of the present invention, based on lake chlorophyll a remote sensing monitoring ultimate principle, determine mineral suspensions in water body and yellow substance insensitive chlorophyll a Monitoring Index BNDBI, by this core of quantitative relationship of chlorophyll-a concentration and the BNDBI of situ measurements of hyperspectral reflectance and the BNDBI data after Rayleigh scattering is corrected, realize estimating the high precision of MODIS image Determination of Chlorophyll a, the spatial and temporal distributions of objective reality ground reflection lake chlorophyll a more.The high precision monitor of chlorophyll a effectively can realize lake algal tufa risk and carry out Efficient Evaluation to watershed; The long-term high precision monitor of lake chlorophyll-a concentration; contribute to change and the development trend thereof of chlorophyll a between Scientific evaluation year border; the performance of the improvement of Efficient Evaluation lake pollution and restoration of the ecosystem, for the water resources management of the department such as water conservancy, environmental protection, the science decision of water environment protection provide science and technology support.

As long as should be appreciated that aforementioned concepts and all combinations of extra design described in further detail below can be regarded as a part for subject matter of the present disclosure when such design is not conflicting.In addition, all combinations of theme required for protection are all regarded as a part for subject matter of the present disclosure.

The foregoing and other aspect of the present invention's instruction, embodiment and feature can be understood by reference to the accompanying drawings from the following description more all sidedly.Feature and/or the beneficial effect of other additional aspect of the present invention such as illustrative embodiments will be obvious in the following description, or by learning in the practice of the embodiment according to the present invention's instruction.

Accompanying drawing explanation

Accompanying drawing is not intended to draw in proportion.In the accompanying drawings, each identical or approximately uniform ingredient illustrated in each figure can represent with identical label.For clarity, in each figure, not each ingredient is all labeled.Now, the embodiment of various aspects of the present invention also will be described with reference to accompanying drawing by embodiment, wherein:

Fig. 1 is the basic principle schematic of BNDBI Index Monitoring chlorophyll a.

Fig. 2 is quantitative relationship between BNDBI and chlorophyll-a concentration under theoretical modeling.

Fig. 3 is different aerosol type and thickness thereof, different sun altitude, moonscope angle and position angle time, the quantitative relationship between the BNDBI based on Rrs and the BNDBI based on Rrc.

Fig. 4 is MODIS satellite high-precision monitoring spatial distribution result (on September 25th, 2012) of Chaohu chlorophyll a.

Fig. 5 is the application schematic diagram that BNDBI algorithm affects for high suspended matter.

Fig. 6 is the application schematic diagram that BNDBI algorithm affects for Bao Yun.

Fig. 7 is the application schematic diagram that BNDBI algorithm affects for solar flare.

In aforementioned diagram 1-7, as English form express each coordinate, mark or other represent, be known in the field, do not repeat again in this example.

Embodiment

In order to more understand technology contents of the present invention, institute's accompanying drawings is coordinated to be described as follows especially exemplified by specific embodiment.

Each side with reference to the accompanying drawings to describe the present invention in the disclosure, shown in the drawings of the embodiment of many explanations.Embodiment of the present disclosure must not be intended to comprise all aspects of the present invention.Be to be understood that, multiple design presented hereinbefore and embodiment, and those designs described in more detail below and embodiment can in many ways in any one is implemented, this should be design disclosed in this invention and embodiment is not limited to any embodiment.In addition, aspects more disclosed by the invention can be used alone, or otherwisely anyly appropriately combinedly to use with disclosed by the invention.

The present invention gives MODIS satellite data and carries out high precision monitor to eutrophic lake chlorophyll-a concentration, and above-mentioned purpose is achieved in that

Screen chlorophyll-a concentration sensitive and don't the chlorophyll a evaluation number (BNDBI) that affects by high suspended matter, Bao Yun, solar flare;

On the basis of bio-optical model simulation, prove the quantitative relationship between BNDBI and chlorophyll-a concentration theoretically;

In conjunction with water spectral information and the corresponding water body chlorophyll alpha concentration of the field monitoring of 2013-2014 Chaohu, obtain the Retrieving Chlorophyll-a Concentration algorithm based on situ measurements of hyperspectral reflectance and BNDBI;

By simulating different aerosol type and thickness, different sun altitude, moonscope angle and position angle, obtain based on R _rsbNDBI with based on R _rcbNDBI between 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 of correcting through Rayleigh scattering, and then estimate lake full waters chlorophyll-a concentration and space distribution thereof.

The exemplarily description of property, shown in accompanying drawing, is specifically described the enforcement of preceding method

Step 1, determine the monitoring and evaluation index BNDBI of chlorophyll a

On chlorophyll-a concentration sensitive and don't refer to based on chlorophyll a and suspension spectral response characteristics by the chlorophyll a evaluation number BNDBI that high suspended matter, Bao Yun, solar flare affect, with blue wave band and near-infrared band for baseline, select red, green wave band standard deviation form, the adverse effect that high suspended matter, Bao Yun, solar flare are estimated chlorophyll-a concentration can be avoided, and using this algae index as chlorophyll-a concentration remote sensing monitoring index.

Particularly, based on water body optically active substance (chlorophyll a, mineral suspensions, yellow substance) fundamental surveillance principle, the spectral signature of three kinds of optically active substances in research water body, and in conjunction with the inversion algorithm of existing chlorophyll a in case Ⅱ waters, comparative analysis is relative merits separately, while selecting accurately to estimate chlorophyll-a concentration, be not subject to the cardinal index of Monitoring Index as blue-green alga bloom MODIS satellite monitoring of the impact of mineral suspensions and yellow substance in water body, to overcome the rough sledding of other optical active matter confrontation chlorophyll-a concentrations monitoring in water body.

In the present embodiment, because the water body of high chlorophyll a has individual reflection peak at green light band (570nm), and to cause the reflection paddy (Fig. 1) of red spectral band in the strong absorption of 665nm because of chlorophyll a, therefore, the content of chlorophyll a can be estimated from the chlorophyll a characteristic wave bands that MODIS is corresponding.Fig. 1 is high chlorophyll a under MODIS band setting, high muddiness and the general spectrum of water body and the difference of three, can find out if with 555nm, 645nm wave band for two ends basic point, high chlorophyll a water body and high feculent water body have maximum difference, in addition, be baseline based on blue wave band (469nm) and near-infrared band (859nm), can the impact that causes of the certain thin cloud of filtering and solar flare.According to this feature, propose BNDBI (Baseline Normalizeddifference bloom index) index:

BNDBI＝(Rrs555'-Rrs645')/(Rrs555'-Rrs645')

$\mathrm{Rrs}{555}^{\′}=\mathrm{Rrs}555-[\mathrm{Rrs}469\×\frac{(859-555)}{(859-469)}+\mathrm{Rrs}859\×\frac{(555-469)}{(859-469)}]---\left(1\right)$

$\mathrm{Rrs}{645}^{\′}=\mathrm{Rrs}645-[\mathrm{Rrs}469\×\frac{(859-645)}{(859-469)}+\mathrm{Rrs}859\×\frac{(645-469)}{(859-469)}]$

Wherein, R _rs(λ) be the λ wavelength place water body remote sensing reflectance that ground survey obtains.

The simulation of step 2, bio-optical model proves the quantitative relationship between BNDBI and chlorophyll-a concentration

On the basis of bio-optical model, in conjunction with the measured data in Chaohu, carry out the numerical simulation under different sight, prove the quantitative relationship of BNDBI and chlorophyll-a concentration theoretically, and in water body, other optical active matters this algorithm of verifying affects.

In the present embodiment, for general water body, the remote sensing reflectance of water body and the inherent optics attribute of water body proportional,

$\mathrm{Rrs}\left(\mathrm{\λ}\right)\∝\frac{b_b\left(\mathrm{\λ}\right)}{a\left(\mathrm{\λ}\right)+b_b\left(\mathrm{\λ}\right)}$

a(λ)＝a _w(λ)+a _ph(λ)+a _d(λ)+a _g(λ)

b _b(λ)＝b _bw(λ)+b _bp(λ) (2)

Wherein a _wand b _bwcorrespond to absorption coefficient and the backscattering coefficient of pure water; And a _ph, a _dand a _gbe then the absorption coefficient of phytoplankton dynamic processes, mineral suspensions and yellow substance, they all also exist substantial connection, b with the amount of respective substance in water body _bpthe backscattering coefficient of particle in water body, in the water body that algae content is not high, this coefficient and mineral suspensions close relation.Wherein,

$a_{\mathrm{ph}}\left(\mathrm{\λ}\right)=a_{\mathrm{ph}}^{*}\left(\mathrm{\λ}\right)\×\mathrm{Chla}---\left(3\right)$

According to formula (1), between BNDBI and chlorophyll-a concentration, there is following relation,

$\mathrm{BNDBI}=\frac{\mathrm{Rrs}{\left(555\right)}^{\′}-\mathrm{Rrs}{\left(645\right)}^{\′}}{\mathrm{Rrs}{\left(555\right)}^{\′}+\mathrm{Rrs}{\left(645\right)}^{\′}}=\frac{{\mathrm{\α}}_1\×{\mathrm{Chla}}^3+{\mathrm{\α}}_2\×{\mathrm{Chla}}^2+{\mathrm{\α}}_3\×\mathrm{Chla}+{\mathrm{\α}}_4}{{\mathrm{\β}}_1\×{\mathrm{Chla}}^3+{\mathrm{\β}}_2\×{\mathrm{Chla}}^2+{\mathrm{\β}}_3\×\mathrm{Chla}+{\mathrm{\β}}_4}---\left(4\right)$

According to formula (4), there is monotonic relationshi between BNDBI and chlorophyll-a concentration, namely BNDBI increases with the increase of chlorophyll-a concentration.Therefore, assuming that mineral suspensions concentration is 50mg/L in water body, when ignoring yellow substance and affecting, Fig. 2 be based on bio-optical model simulation BNDBI and chlorophyll-a concentration between quantitative relationship.

According to our 2013-2014 in the spectroscopic data of the fieldwork in Chaohu and corresponding chlorophyll-a concentration data, we construct the inversion algorithm of Chaohu chlorophyll a based on measured spectra data Rrs.

Chla＝982.3*BNDBI ⁴+71.86*BNDBI ³+562.4*BNDBI ²+79.05*BNDBI+6.6(5)

Step 3, to obtain based on situ measurements of hyperspectral reflectance data R _rsbNDBI with correct based on the Rayleigh scattering simulated after R _rcbNDBI between quantitative relationship

Investigate Chaohu Prefecture at different aerosol type and thickness, different sun altitude, moonscope angle and position angle to based on situ measurements of hyperspectral reflectance data R _rsbNDBI correct afterwards R with based on Rayleigh scattering simulate _rcbNDBI between the impact of quantitative relationship, and by quantitative model that simulated data is determined between the two.

In the present embodiment, the inversion algorithm obtaining chlorophyll a be extended to satellite image data based on measured spectra data, air is corrected and be can not ignore.But still lack for the effective accurately air correction algorithm of high feculent water body at present, this adopts the Rayleigh scattering of MODIS image to correct, namely by this rectification, the optical information on atmospheric envelope top eliminates the impact of Rayleigh scattering, still contains gasoloid information and terrestrial information.Data after correcting based on Rayleigh scattering, BNDBI is expressed as:

$\mathrm{BNDBI}=\frac{\mathrm{Rrc}{555}^{\′}-\mathrm{Rrc}{645}^{\′}}{\mathrm{Rrc}{555}^{\′}+\mathrm{Rrc}{645}^{\′}}$

$\mathrm{Rrc}{555}^{\′}=\mathrm{Rrc}555-[\mathrm{Rrs}469\×\frac{(859-555)}{(859-469)}+\mathrm{Rrs}859\×\frac{(555-469)}{(859-469)}]$

$\mathrm{Rrc}{645}^{\′}=\mathrm{Rrc}645-[\mathrm{Rrs}469\×\frac{(859-645)}{(859-469)}+\mathrm{Rrs}859\×\frac{(645-469)}{(859-469)}]---\left(6\right)$

Wherein, R _rc(λ) reflectivity at the λ wavelength place that Rayleigh corrects is through.R _rcbe that MODIS data carry out Rayleigh scattering correction, the research then based on (2004) such as Hu is converted into the reflectivity after Rayleigh scattering correction:

$R_{\mathrm{rc}}=\mathrm{\π}{L}_t^{*}/\left(F_0\cos {\mathrm{\θ}}_0\right)-R_r---\left(7\right)$

In formula, the sensor radiation rate after correcting ozone and other gettering effects, F ₀the outer solar irradiance of air sphere when obtaining data, θ ₀solar zenith angle, R _rit is the Ruili reflectivity adopting 6S (Vermote etc., 1997) to predict.

Based on radiation transfer theory and a supposition uncoupled ocean-Atmosphere System, R _rccan be expressed as:

R _rc＝R _a+t ₀tR _target(8)

In formula, R _agasoloid reflectivity (comprising the interaction coming from aerosol particles), R _targetthe surface reflectivity of fieldwork target (algae or water body), t ₀be the atmospheric transmissivity from the sun to object, t is the atmospheric transmissivity from object to satellite sensor.Due to the impact of wind-engaging and current, planktonic algae presents a kind of form of oil slick usually, and therefore t can regard the light transmission of planktonic algae as.

In order to investigate different aerosol type and thickness thereof, and the impact that moonscope causes, we according to Chaohu Prefecture at different aerosol type and thickness, different sun altitude, moonscope angle and position angle to based on situ measurements of hyperspectral reflectance data R _rsbNDBI correct afterwards R with based on Rayleigh scattering simulate _rcbNDBI between the impact (Fig. 3) of quantitative relationship, and by the quantitative model that simulated data is determined between the two,

BNDBI(R _rc)＝1.051BNDBI(R _rs)-0.007

(9)

Step 4, obtain chlorophyll-a concentration inversion algorithm based on MODIS satellite image

Based on abovementioned steps and method, by the Retrieving Chlorophyll-a Concentration algorithm application based on situ measurements of hyperspectral reflectance data to the satellite image data of correcting through Rayleigh scattering, based on aforesaid inversion algorithm method, obtain after to several seasonal effect in time series satellite image process the year border of eutrophic lake chlorophyll-a concentration, the moon border Changing Pattern and space distribution thereof.

Based on formula (5) and formula (9), the MODIS satellite high-precision inverse model of Chaohu chlorophyll a can be obtained.

Correct according to the Rayleigh scattering based on MODIS image, convolution (5) and formula (9) can realize the high precision estimation of total image Determination of Chlorophyll a concentration.Idiographic flow is mainly as follows: 1. carried out geometric correction and radiation calibration calculating to the MODIS image obtained.Geometric correction adopts Geographic Lat/Lon projection, and correct in conjunction with the latitude and longitude information in 1B data, the positional precision after correction reaches 0.5 pixel.In ERDAS, utilize vector border, lake, extract lake waters by mask technique, the impact of removing island vegetation, utilizing nearest neighbor method, is 250m by the resampling of MODIS 500m image data; 2. in MODIS image, pixel calculates its R at band 1 (645nm), band 2 (859nm), band 3 (469nm) and band 4 (555nm) one by one _rcvalue; 3. according to formula (6) pixel calculating BNDBI value one by one; 4. then according to formula (5) and formula (9), the chlorophyll a spatial distribution result (see Fig. 4) after calculating can be obtained.

In addition, under this baseline algorithm has also been carried out high feculent water body, thin cloud and solar flare condition by us, chlorophyll a retrieving concentration contrasts, and result respectively as shown in Figure 5-Figure 7, is compared and found that this model still has higher precision, have certain universality.

Can realize the algal tufa area reckoning to algal tufa mixed pixel in a certain MODIS image by said method, the high precision of objective reality ground reflection lake algal tufa area is estimated and spatial and temporal distributions more.The high precision monitor of blue-green algae area effectively can realize lake algal tufa risk and carry out Efficient Evaluation to watershed; In addition; after MODIS history image is calculated one by one by said method; the long-term high precision monitor of lake blue algae area can be realized; contribute to change and the development trend thereof of algal tufa actual strength between Scientific evaluation year border; the performance of the improvement of Efficient Evaluation lake pollution and restoration of the ecosystem, for the water resources management of the department such as water conservancy, environmental protection, the science decision of water environment protection provide science and technology support.

Although the present invention with preferred embodiment disclose as above, so itself and be not used to limit the present invention.Persond having ordinary knowledge in the technical field of the present invention, without departing from the spirit and scope of the present invention, when being used for a variety of modifications and variations.Therefore, protection scope of the present invention is when being as the criterion depending on those as defined in claim.

Claims (6)

1. a MODIS satellite high-precision monitoring method for nutrition-enriched water of lake chlorophyll a, is characterized in that, said method comprising the steps of:

1) screening is on chlorophyll-a concentration sensitive and don't the chlorophyll a evaluation number BNDBI that affects by high suspended matter, Bao Yun, solar flare;

Wherein, described on chlorophyll-a concentration sensitive and don't refer to based on chlorophyll a, mineral suspensions and yellow substance spectral response characteristics by the chlorophyll a evaluation number BNDBI that high suspended matter, Bao Yun, solar flare affect, with blue light and near-infrared band for baseline, select the difference expression-form between red, green wave band, the adverse effect that high suspended matter, Bao Yun, solar flare are estimated chlorophyll-a concentration can be avoided, and using this algae index as chlorophyll-a concentration remote sensing monitoring index;

2) based on bio-optical model simulation, the quantitative relationship between BNDBI and chlorophyll-a concentration is specified;

On the basis of bio-optical model, in conjunction with the measured data in lake, carry out the numerical simulation under different sight, determine the quantitative relationship of BNDBI and chlorophyll-a concentration, determine this exponent pair height feculent water body insensitivity theoretically simultaneously;

3) ground monitoring remote sensing reflectance R is obtained _rsr after correcting with the Rayleigh scattering of simulation _rcbetween quantitative relationship;

Simulate lakeland under different aerosol type and thickness, different sun altitude, moonscope angle and position angle situation, based on the remote sensing reflectance R of ground monitoring _rsbNDBI and the R to correct based on the Rayleigh scattering simulated _rcbNDBI between quantitative relationship;

4) the chlorophyll-a concentration inversion algorithm based on MODIS satellite image is obtained;

Based on abovementioned steps and method, by the Retrieving Chlorophyll-a Concentration algorithm application based on situ measurements of hyperspectral reflectance data to the satellite image data of correcting through Rayleigh scattering, based on aforesaid inversion algorithm method, obtain after to several seasonal effect in time series satellite image process the year border of eutrophic lake chlorophyll-a concentration, the moon border Changing Pattern and space distribution thereof.

2. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, it is characterized in that, in described step 1), chlorophyll a, mineral suspensions and yellow substance spectral response characteristics come from the spectroscopic data R of lake fieldwork _rs, the monitoring instrument of employing is the binary channels ground light spectrum monitoring instrument of ASD company of the U.S..

3. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, is characterized in that, described step 2) in, the chlorophyll a evaluation number BNDBI expression-form based on situ measurements of hyperspectral reflectance data is:

。

4. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, is characterized in that, described step 2) in " carrying out the numerical simulation of different sight " specifically comprise:

First, when mineral suspensions concentration and yellow substance remain unchanged, the quantitative relationship between BNDBI and chlorophyll-a concentration is obtained;

Secondly, simulation chlorophyll a and yellow substance concentration constant time, BNDBI is to the response of mineral suspensions concentration;

When finally simulation chlorophyll a and mineral suspensions concentration remain unchanged, yellow substance concentration change is on the impact of BNDBI.

5. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, it is characterized in that, in described step 3), aerosol type is with reference to the result of the LUT of SeaDas, gasoloid thickness is with reference to lakeland long-term monitoring result scope, and moonscope angle is then determined according to the relative position in the sun, satellite and lake.

6. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, is characterized in that, in described step 4), the BNDBI index expression-form being applied to MODIS image is:

；

；

；

Further, the BNDBI index of MODIS image is based upon on the basis that the radiation calibration of MODIS satellite image, geometric correction and air Rayleigh scattering correct.