CN108896502B - Construction method of layered water body biological optical model - Google Patents
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Abstract
The invention relates to a construction method of a layered water body biological optical modelThe method includes simulating underwater light field distribution under different vertical algae particle distributions by means of Hydrolight radiation transmission, obtaining an apparent optical parameter data set, and analyzing remote sensing reflectance r of the vertical algae particle distributions to characterize the underwater light fieldrs(lambda, z) and the diffuse attenuation coefficient Kx(λ, z); then, the equivalent weight functions are compared and screened, and the remote sensing reflectance (r) of the water bodies in different layers to the water surface is calculatedrs(0‑) Contribution rate of); and finally, constructing layered water body biological optical models under different sun angles, and verifying the models by using simulation data. The layered water body biological optical model constructed by the method is a premise and a foundation for improving the remote sensing inversion precision of the water quality parameters of the eutrophic lake, can supplement and perfect the basic theory and method of lake water color remote sensing, promotes the development of the lake water color remote sensing, and has great scientific significance.
Description
Technical Field
The invention relates to a construction method of a layered water body biological optical model.
Background
Lake eutrophication and harmful algae bloom are water area ecological environment problems (Qinbei strong, et al, 2016) generally faced all over the world, and frequent outbreak of blue algae bloom caused by lake eutrophication is a major challenge faced by the current Chinese freshwater lakes (Liu and Yang, 2012). Under the coupling driving of various environmental factors (exogenous factors), blue algae generate huge biomass due to the unique physiological and ecological characteristics (endogenous factors) and account for the absolute advantage in phytoplankton. Under appropriate hydrometeorology conditions, a large number of algal particles accumulate on the water meter to form cyanobacterial bloom (Ma Jian Rong et al, 2013). Research shows that the cyanobacterial bloom has strong space-time variability and large change of the area of the cyanobacterial bloom in a short time (Yang et al, 2013; Zhang et al, 2015).
At present, as an effective research means, water color remote sensing has been widely applied to the aspects of water quality parameter inversion of eutrophic lakes, monitoring of algal bloom exposure area and frequency, and the like. However, most of the traditional lake water color remote sensing is established based on the assumption of vertical uniformity of water, and the vertical non-uniform distribution and rapid change of algae particles in the formation process of algal blooms pose great challenges to the application of the assumption. The existing research is limited to the judgment of the vertical distribution type of the algae particles through the remote sensing reflectance above the water surface or the remote sensing reflectance correction based on an empirical method to reduce the influence of the vertical distribution of the algae particles. It is still difficult to reveal the mechanism of remote sensing monitoring of the vertical distribution of algal particles. The radiation transmission theory describes the physical process of a large number of photons entering a water body, and finally reaching a sensor through the absorption and scattering action of each particle in the water.
The underwater light field simulation is a process of truly restoring the light field distribution in the water body based on the radiation transmission theory. By simulating the underwater light field under the condition of different vertical distributions of the algae particles, the influence mechanism of the vertical distribution of the algae particles on the underwater light field is explored, and the method is the core for realizing quantitative remote sensing monitoring of the vertical distribution of the algae particles. Taking an eutrophic lake-Taihu lake as an example, the change rule of the vertical distribution of algae particles and the influence mechanism of the change rule on an underwater light field in the formation process of the clear algae bloom are needed to research the remote sensing monitoring mechanism of the vertical distribution of the algae particles. The influence mechanism of the vertical distribution of the algae on the apparent optical parameters of different layers of the underwater light field is clarified, the quantitative contribution of the inherent optical parameters of the different layers of the underwater light field to the remote sensing reflectance is calculated, and a biological optical model of the stratified water body is established.
Reference documents:
Liu J.,W.Yang.2012.Water sustainability for China and beyond.Science,337(6095):649-650.
Millán-
R.,S.Alvarez-Borrego,and C.C.Trees.1997.Modeling thevertical distribution of chlorophyll in the California Current System.Journalof Geophysical Research:Oceans,102(C4): 8587-8595.
Yang,L.,K.Lei,W.Meng,G.Fu,and W.Yan.2013a.Temporal and spatialchanges in nutrients and chlorophyll-a in a shallow lake,Lake Chaohu,China:An11-year investigation.Journal of Environmental Sciences,25(6):1117-1123.
Zhang,Y.,R.Ma,M.Zhang,H.Duan,S.Loiselle,and J.Xu.2015.Fourteen-yearrecord (2000–2013)of the spatial and temporal dynamics of floating algaeblooms in Lake Chaohu, observed from time Series of MODIS images.RemoteSensing,7(8):10523-10542.
qinbei, Yangguijun, Majiarong, et al 2016. dynamic characteristics of Taihu lake blue algae bloom 'outbreak' and its mechanism [ J ] scientific report 07:759-770.
Ma Jianrong, Deng Jian Ming, Qinbei Qiang, 2013, research on the occurrence mechanism of blue-green algae blooms in lakes [ J ] ecological report [ 33(10): 3020-.
Disclosure of Invention
The invention aims to provide a construction method of a layered water body biological optical model.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a construction method of a biological optical model of a stratified water body comprises the following steps:
1) simulating the distribution of the underwater light field under different alga vertical distribution conditions through the simulation of Hydrolight radiation transmission to obtain an apparent optical parameter data set, and analyzing the influence mechanism of the alga vertical distribution on the spectral characteristics and the vertical distribution of the apparent optical quantity AOP representing the underwater light field;
wherein the apparent optical quantity AOP comprises a remote reflectance rrs(lambda, z) and a diffuse attenuation coefficient Kx(λ,z)；
2) Calculating the remote sensing reflectance r of different layers of the water body to the position just below the water surfacers(0-) The contribution rate of (c);
screening an equivalent weight function according to the analog value obtained in the step 1), and quantitatively calculating each layer pair r in the water bodyrs(0-) The contribution rate of (c);
3) constructing a layered water body biological optical model under different sun angle conditions;
according to the step 2), different layer pairs r of the stratification water bodyrs(0-) The intrinsic optical parameters IOP and r of different depths are constructedrs(0-) A relationship model between them.
In the step 1), the vertical distribution of the algae is represented by the vertical distribution of phytoplankton pigment represented by chlorophyll a concentration; the chlorophyll a concentration was measured in an acetone lab using a spectrophotometer;
the chlorophyll a concentration function expression of the vertical nonuniform distribution of the algae is as follows:
wherein, C0H and sigma are parameters of a chlorophyll a distribution function and are obtained through function fitting.
The remote sensing reflectance r of different layers of water under the water surface in the step 2)rs(0-) The method for calculating the contribution ratio is as follows:
in the formula, Frz1,z2Is the water body pair r at the depth z1-z2rsContribution of (0-), g (z') is the equivalent weight function, bb(z) is the backscattering coefficient at depth z, and a (z) is the absorption coefficient at depth z.
The layered water body biological optical model under different sun angle conditions in the step 3) is as follows:
wherein S isiIs the depth z (i), the coefficient at the wavelength λ, bb(λ, z (i)) is a depth z (i) and a backscattering coefficient at a wavelength λ, a (λ, z (i)) is a depth z (i) and an absorption coefficient at a wavelength λ, i is an i-th layer, and n is a total number of layers.
The layered water body biological optical model constructed by the method is a premise and a foundation for improving the remote sensing inversion precision of the water quality parameters of the eutrophic lake, can supplement and perfect the basic theory and method of lake water color remote sensing, promotes the development of the lake water color remote sensing, and has great scientific significance.
Drawings
The drawings are not intended to be to scale, wherein each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral and not every component may be labeled in every figure for clarity, 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 an example of a vertical profile of chlorophyll-a concentration, absorption coefficient, backscattering coefficient and IOP;
FIG. 2 is a graph of the effect of vertical algae distribution on the remote reflectance spectra of different layers below the water surface;
FIG. 3 is a graph of the effect of vertical distribution of algae on vertical distribution of remote reflectance and the ratio (G) of remote reflectance to IOP for different layers below the water surface;
FIG. 4 shows the vertical distribution of algae versus the diffuse attenuation coefficient KdThe influence of the spectrum;
FIG. 5 shows the vertical distribution of algae versus the diffuse attenuation coefficient Kd、Ku、KLuThe influence of vertical distribution of (a);
FIG. 6 is a comparison of different equivalent weight functions at different bands;
FIG. 7 is a water body pair r of different layersrs(0-);
FIG. 8 is a result of verifying a biological optical model of a stratified water body;
in the above-mentioned diagrams 1 to 8, 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 defined 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, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Example 1
This example illustrates the method for constructing a layered water biological optical model according to the present invention.
In this embodiment, a biological optical model of a stratified water body is established based on radiation transmission simulation data, and the implementation manner is as follows: simulating underwater light field data sets under different chlorophyll a concentration vertical distribution conditions through Hydrolight radiation transmission simulation, and analyzing the apparent optical quantity AOP (remote sensing reflectance r) of the algae vertical distribution to the characterization underwater light fieldrs(lambda, z), diffuse attenuation coefficient Kx(λ, z)) and the vertical influence mechanism; by comparing the using effects of several different equivalent weight functions in the simulation data set, the equivalent weight function with the minimum error is screened out, and the remote sensing reflectance (r) of different layers of the water body to the water surface is calculatedrs(0-) Contribution rate of); and finally, establishing a layered water body biological optical model considering the vertical distribution of algae under the condition of different sun angles, giving out coefficient lookup tables of different sun angles and different wavelengths, and verifying by using simulation data.
The implementation of the foregoing method is specifically described below, as an exemplary description, with reference to the figures.
Step 1) analyzing the influence mechanism of different vertical distributions of algae on an underwater light field;
the vertical distribution of the algae is characterized by the vertical distribution of phytoplankton pigment characterized by chlorophyll a concentration, the chlorophyll a concentration is measured by using a spectrophotometer in an acetone method laboratory, and the chlorophyll a concentration function expression of the vertical non-uniform distribution of the algae is as follows:
wherein, C0H and sigma are parameters of a chlorophyll a distribution function and are obtained through function fitting.
Simulating underwater light field data sets under different chlorophyll a concentration vertical distribution conditions by virtue of Hydrolight radiation transmission simulation, wherein the simulated wavelength range is 400: 5: 750 nm; set of C0The range 0: 5: 40 μ g/L, h range 1: 5: 76 μ g/L, σ range 0.2: 0.2: 1.4 m; hydrolight input variables include solar altitude (θ, 15: 15: 75 °), wind speed (2.25m/s), SPIM inorganic suspended particulate matter (0: 5: 60mg/L), absorption coefficient of CDOM at 440nm (ag(440)，0：0.5：4.0m-1) And the spectral slope Sg(0.019); the output data of Hydrolight is the apparent optical quantity AOP (remote reflectance r)rs(lambda, z) and a diffuse attenuation coefficient Kx(λ,z))；
FIG. 1 shows the input chlorophyll a concentration, absorption coefficient a (550), and backscattering coefficient bb(550) And the vertical profile example of the intrinsic optical characteristic IOP, a 1008 group of data simulations were performed. L1-L6 in FIG. 1 indicate different parameters (C)0H, σ); wherein L1(10,16,0.6), L2(10,31,0.6), L3(10,16,0.2), L4(10,31,0.2), L5(5,16,0.2), L6(5, 31, 0.2).
Then, the vertical distribution of the algae is analyzed to characterize the AOP (remote sensing reflectance r)rs(lambda, z), diffuse attenuation coefficient Kx(lambda, z)) and the mechanism of influence of the vertical distribution, as shown in FIGS. 2-5, it can be seen that ① the vertical non-uniform distribution of algae mainly affects r in the green-red wavelength rangers(z), as can be seen from L1-L6, r for different Chla vertical profilesrsIncreases with increasing water depth; the differences in different water depths are mainly concentrated in the green and red bands. The maximum wavelength occurs at 590-600nm, the difference between the surface layer and the subsurface layer of L6 is 0.014sr-1Almost equal between the surface and subsurface of L1 ② for r at water depths where Chla varies more (with greater Chla disparity)rs③ is low (C)0) The smaller, thersThe greater the effect of (c). L5-L6 in comparison with L3-L4, r of L5-L6rsThe variation was significantly greater than L3-L4.
KdAnd KuIs an important parameter for representing the change rule of the underwater light field. Alga vertical non-uniform distribution pair K under different depth and wavelength conditions x① is vertically non-uniform, KxThe vertical distribution rule is as follows: has a reaction with rrsSimilar to the law, Kx tends to be stable when Chla is unchanged; when the vertical difference is not large, KxThe change along with the vertical direction is not large; when the vertical difference is large, KxThe change is large, and is directly related to the Chla curve ② from L1-L6, when the vertical change is not large, K isdAnd KuA small difference of 2Kd(ii) a But with large vertical variation, KdAnd Ku、KLuThe difference at different depths is large and cannot be simply considered as 2Kd(ii) a Wherein K Lu③ theoretically, Kd+KLuIs more reasonable. Evidence of 2K from the datadIs not good, Kd+KLuCloser to the theoretical value.
Step 2) calculating the remote sensing reflectance (r) of different layers of the water body to the water surface below the water surfacers(0-) Contribution rate of);
screening a proper equivalent weight function according to the influence mechanism of the vertical nonuniform distribution of the algae on the AOP of different layers of the underwater light field in the step 1), and quantitatively calculating the pairs r of each layer in the water bodyrs(0-) The contribution rate of (c);
remote sensing reflectance r of different layers of water to the water surfacers(0-) The method for calculating the contribution ratio is as follows:
in the formula, Frz1,z2Is a depth z1-z2The contribution rate of the water body to rrs (0-), g (z') is an equivalent weight function, bb(z) is the backscattering coefficient at depth z, a(z) is the absorption coefficient at depth z.
The present invention compares 5 equivalent weight functions, as shown in FIG. 6, (a) is the average relative error of all the simulated data at 490, 550 and 675nm wavelengths; (b) is a weighted average r under the condition of L6 obtained by using 5 equivalent weight functionsrsA numerical value; Z-KdKLuFinger adopts Kd+KLuThe equivalent weight function of (Zaneveldet et al (2005)); Z-2KdFinger adopts 2KdThe equivalent weight function of (Zaneveldet et al (2005)); GC-KdKLuFinger adopts Kd+KLuEquivalent weight functions of (Gordon and Clark (1992)); GC-2KdFinger adopts 2KdEquivalent weight functions of (Gordon and Clark (1992)); s refers to the equivalent weight function in Sokoletsky and Yacobi (2011).
Wherein Z-KdKLuCan be used to calculate the quantitative contribution of the different layers.
Different layer pairs rrs(0-) The quantitative contribution of (fig. 7) indicates that: when the algae are vertically uniform, under different Chla concentrations, the contributions of different layers are inconsistent; the higher the Chla, the larger the contribution ratio of the surface layer, and the smaller the contribution ratio of the subsurface layer; at lower Chla, the light penetration depth is deeper and the subsurface signal enters the sensor, resulting in increased subsurface contribution. When the algae is not vertically uniform: r under different wavelengths and different structural parametersrsThe contribution of (1) is relatively large, the contribution of the surface layer is 30% -85%; the contribution of the subsurface layer is around 20% and the contribution of the third layer is around 10%. That is, in the case where the maximum value is located at the surface layer, the surface layer and subsurface layer pair rrsThe contribution rate of the catalyst can reach 90 percent. The larger the local low value of the water body, the less vertical difference in Chla, thus also increasing the surface contribution and decreasing the subsurface contribution. And as the depth increases, the influence of the structural parameters of the Chla vertical distribution curve on the contribution rate of the surface layer is weakened.
Step 3) constructing layered water body biological optical models under different sun angle conditions;
different layer pairs r of stratified waterrs(0-) The contribution of (D) illustrates the different depth pairs rrs(0-) The contribution rate of the optical model is decreased with the depth, and accordingly, a layered water body biological optical model considering the inherent optical parameters of different depths is established.
R according to the weighted average theory and equation (2)rs(0-) R at different water depths of vertically non-uniformly distributed water bodyrs-v(0-) And IOP (z), Fr(z) and g (z') are related.
Wherein:
suppose that:
Sicoefficients representing the ith layer;
IOP (lambda, z) and S can be utilizedi(lambda, z) expression rrs-v(λ,0-) The following are:
namely:
wherein S isiIs the depth z (i), the coefficient at the wavelength λ, bb(λ, z (i)) is a depth z (i) and a backscattering coefficient at a wavelength λ, a (λ, z (i)) is a depth z (i) and an absorption coefficient at a wavelength λ, i is an i-th layer, and n is a total number of layers. The determination of the model coefficients was obtained by curve fitting functions in MATLAB 2015b, and table 1 below is a look-up table of model parameters at different solar angles and different wavelengths.
Table 1 layered water body biological optical model coefficient lookup table (different wavelength, different sun angle)
Finally, the model is verified by using reserved 1/3 data (fig. 8), the effect is better, and the APD is under the conditions of three solar altitude angles of 30 degrees, 45 degrees and 60 degrees at three wavelengths of 490 nm, 550 nm and 675nm<2％,R2>0.97。
Claims (3)
1. A construction method of a biological optical model of a stratified water body is characterized by comprising the following steps:
1) simulating the distribution of the underwater light field under different alga vertical distribution conditions through the simulation of Hydrolight radiation transmission to obtain an apparent optical parameter data set, and analyzing the influence mechanism of the alga vertical distribution on the spectral characteristics and the vertical distribution of the apparent optical quantity AOP representing the underwater light field;
wherein the apparent optical quantity AOP comprises a remote reflectance rrs(lambda, z) and a diffuse attenuation coefficient Kx(λ,z)；
2) Calculating the remote sensing reflectance r of different layers of the water body to the position just below the water surfacers(0-) The contribution rate of (c);
screening an equivalent weight function according to the analog value obtained in the step 1), and quantitatively calculating each layer pair r in the water bodyrs(0-) The contribution rate of (c); the calculation method is as follows:
in the formula, Frz1,z2Is the water body pair r at the depth z1-z2rsContribution of (0-), g (z') is the equivalent weight function, bb(z) is the backscattering coefficient at depth z, a (z) is the absorption coefficient at depth z;
3) constructing a layered water body biological optical model under different sun angle conditions;
according to the step 2), different layer pairs r of the stratification water bodyrs(0-) The intrinsic optical parameters IOP and r of different depths are constructedrs(0-) The relationship model between, as follows:
wherein S isiIs the depth z (i), the coefficient at the wavelength λ, bb(λ, z (i)) is a depth z (i) and a backscattering coefficient at a wavelength λ, a (λ, z (i)) is a depth z (i) and an absorption coefficient at a wavelength λ, i is an i-th layer, and n is a total number of layers.
2. The method according to claim 1, wherein in step 1), the vertical distribution of algae is characterized by the vertical distribution of phytoplankton pigments characterized by chlorophyll-a concentration; the chlorophyll a concentration was measured by an acetone laboratory using a spectrophotometer.
3. The method of claim 2, wherein the chlorophyll-a concentration function expression of the algae vertical heterogeneous distribution is as follows:
wherein, C0H and sigma are parameters of a chlorophyll a distribution function and are obtained through function fitting.
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