A method for optimizing the inherent optical parameters of two types of water bodies

Abstract

In view of the problems that the current remote sensing monitoring of water color three elements lacks a synchronous inversion method for multi-component water color elements, and there are scattered information, complex collection, and untimely analysis and acquisition, the present invention discloses a method for optimizing and simulating the inherent optical parameters of a second type of water body, and the method comprises the following steps: step 1) selecting a study area and designing an experimental plan to measure the chlorophyll a concentration, suspended matter concentration, absorption coefficient of yellow matter at 440nm and water surface spectrum at sampling points; step 2) calculating the absorption coefficient and backscattering coefficient of each sampling point; step 3) simulating the remote sensing reflectivity of each sampling point using the 2SeaColor radiation transmission model; step 4) calculating the root mean square difference between the simulated remote sensing reflectivity and the remote sensing reflectivity of the measured sampling points and determining the value of the inherent optical parameters of the study area.

Description

Technical Field

[0001] The present invention relates to a method for optimizing and simulating the inherent optical parameters of Class II water bodies. The method is aimed at Lake Tai, a typical inland Class II lake in my country. Based on field sampling data and the inherent optical parameters of the study area obtained by optimizing and simulating the 2SeaColor model, high-precision synchronous inversion of water color elements is achieved. Background Art

[0002] New problems such as water resource shortage, water ecological damage, and water environment pollution are becoming increasingly prominent, and water ecological safety management based on the "three water coordination" faces new challenges. Water ecological monitoring is the "pillar" of water ecological environment management and one of the important basic supports for the construction of ecological civilization. The "Notice on Carrying out Pilot Work on Ecological Environment Remote Sensing Monitoring" (Monitoring Letter

[2019] No. 6) issued by the Ministry of Ecology and Environment requires pilot provinces to accelerate the construction of ecological environment remote sensing survey, monitoring and evaluation systems. The "Key River Basin Water Ecological Environment Protection Plan (2021-2025) (Draft for Comments)" clearly points out that it is necessary to formulate an indicator system for coordinating water resources, water ecology, and water environment based on the characteristic elements of the river basin, and to guide and promote high-quality development with high-level protection.

[0003] Surface water bodies are divided into Class I and Class II. Open ocean water bodies are mainly Class I water bodies, and nearshore estuary water bodies are mainly Class II water bodies. Since Class II water bodies are closer to human production and living areas, they are more affected by human activities and are seriously polluted. In recent years, many lakes in my country have experienced eutrophication, organic pollution, shrinking water area, salinization, and ecosystem imbalance (Cheng Qi, 2012). Eutrophication of water bodies will not only destroy the overall function of the lake and the diversity of the lake ecosystem, but also harm phytoplankton and cause algal blooms. The toxins produced by algae affect human health through drinking water plants and threaten human survival. The algae on the water surface emit odors, which seriously affect the landscape of rivers and lakes.

[0004] For a long time, the inversion of the three elements of water color in the second category of nearshore water bodies has been a difficult problem in water color optical remote sensing. The three elements of water color mainly include chlorophyll (Chlorophyll, mainly Chlorophyll-a, Chl-a), non-algae solid suspended matter (Suspended Particulate Matter, SPM) and colored soluble organic matter (Chromophoric Dissolved Organic Matter or Colored Dissolved Organic Material, CDOM) (Tang Junwu et al., 2003). At present, the remote sensing monitoring methods of the three elements of water color are mainly divided into three types: empirical methods, semi-empirical / semi-analytical methods and analytical methods. There are few models for inversion from the perspective of radiation transmission, and they are often based on single-component water color element inversion models, and there are few synchronous inversion models for multi-component water color elements.

[0005] In view of the shortcomings of the current remote sensing monitoring methods of the three elements of water color in Class II water bodies, the present invention focuses on the relationship between the measured chlorophyll a concentration, suspended matter concentration, the absorption coefficient of yellow matter at 440nm and the water surface spectrum, and proposes a method for optimizing the inherent optical parameters of Class II water bodies by establishing a mathematical relationship between the three elements of water color and remote sensing reflectivity, thereby effectively compensating for the discontinuity of traditional monitoring methods in time and space scales, and providing continuous and accurate water quality parameter change data. Summary of the invention

[0006] In view of the problems in the current remote sensing monitoring of water color three elements, such as the lack of a synchronous inversion method for multi-component water color elements, scattered information, complex collection, and untimely analysis and acquisition, the present invention proposes a method for optimizing the simulation of the inherent optical parameters of Class II water bodies, providing a reliable, effective and accurate information source for water ecological protection.

[0007] The purpose of the present invention is achieved through the following technical steps:

[0008] Step 1) Select the study area and design the experimental scheme to measure the chlorophyll a concentration, suspended matter concentration, absorption coefficient of yellow matter at 440nm and water surface spectrum at the sampling point;

[0009] Step 2) calculating the absorption coefficient and backscattering coefficient of each sampling point;

[0010] Step 3) Use the 2SeaColor radiation transfer model to simulate the remote sensing reflectance of each sampling point;

[0011] Step 4) Calculate the root mean square difference between the simulated remote sensing reflectance and the remote sensing reflectance of the measured sampling points and determine the intrinsic optical parameter values ​​of the study area.

[0012] The specific method of step 1) is:

[0013] a) Select the study area; b) Design the lake test plan based on the study area; c) Field measurement of water quality parameters.

[0014] The specific method of step 2) is:

[0015] a) Calculate the absorption coefficient of each sampling point in the study area:

[0016] a(λ)=a W (λ)+a Chla (λ)+a s (λ)+a CDOM (λ)

[0017] Where a(λ) refers to the total absorption coefficient of water at wavelength λ; W (λ) is the absorption coefficient of water molecules, a Chla (λ) is the chlorophyll a absorption coefficient, a s (λ) is the absorption coefficient of suspended matter, a CDOM (λ) is the absorption coefficient of the yellow substance, and the unit is per meter (m -1 ).

[0018] b) Calculate the backscatter coefficient of each sampling point in the study area:

[0019] b b (λ) = b w (λ)+b chla (λ)+b s (λ)

[0020] Where b b (λ) refers to the total scattering coefficient of water at wavelength λ; b w (λ) water molecule scattering coefficient; b Chla (λ) is the scattering coefficient of chlorophyll a; b s (λ) is the scattering coefficient of suspended matter.

[0021] The specific method of step 3) is:

[0022] a) The exponential term x of the intrinsic optical parameters of the study area, the slope n of the suspended matter scattering spectrum, s , and the scattering coefficient b of the suspended matter at 550nm s (550) The three elements are taken as unknown quantities; b) the three unknown quantities are set with reasonable initial values, ranges and step sizes, and the 2SeaColor model is used to continuously cycle to obtain the values ​​corresponding to each group of x, n s , and b s Remote sensing reflectivity of (550).

[0023] Among them, the theoretical formula of the 2SeaColor model is (Salama et al., 2015):

[0024]

[0025] μ w = cosθ s '

[0026]

[0027] In the formula, is the directional hemispherical reflectivity of the semi-infinite medium;

[0028] x is the total backscattering coefficient of the water body b b (Unit: m -1 ) and the total absorption coefficient a (unit: m -1 );

[0029] μ w Refers to the cosine value of the solar zenith angle under a flat water surface; if the solar zenith angle on the water surface is θ s , then θ′ s =arcsin(sinθ s / n w ), where n w is the refractive index of water, which takes a value of 1.33;

[0030] R(0 - ) is the radiance reflectance below the horizontal plane;

[0031] Q is the ratio of the radiance reflectance of the horizontal plane upward to the radiance reflectance of the horizontal plane downward, and the value is 3.25;

[0032] R rs is the reflectivity off water.

[0033] The specific method of step 4) is:

[0034] a) Calculate the root mean square difference (RMSE) between the simulated remote sensing reflectance and the synchronously measured remote sensing reflectance at each sampling point; b) Find the minimum RMSE and determine the intrinsic optical parameter values ​​of the study area. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a method flow chart for implementing the present invention;

[0036] Figure 2 This is the distribution map of the test sampling points;

[0037] Figure 3 This is the simulated remote sensing reflectance map of each sampling point. DETAILED DESCRIPTION

[0038] The following further describes the present invention "a method for optimizing and simulating the inherent optical parameters of two types of water bodies" in conjunction with the accompanying drawings.

[0039] 1. Experimental design and water sample collection

[0040] First, select the study area (taking the nearshore Class II water body Taihu Lake as an example); second, design test sampling points covering the entire lake surface within the study area, such as Figure 2 As shown. In the areas with serious water pollution such as Meiliang Bay and Zhushan Bay in the north of Taihu Lake, the sampling points were increased, and in the areas with cleaner water in the south of Taihu Lake, the sampling points were appropriately reduced. Finally, water sampling bottles were used for water sampling for laboratory measurement of chlorophyll a concentration, suspended matter concentration and absorption coefficient of yellow matter at 440nm, and Fieldspec HandHeld2 handheld portable spectrometer was used for spectral data collection.

[0041] (II) Calculation of absorption coefficient and scattering coefficient

[0042] The absorption coefficient in water takes into account four components in water, namely water molecules, chlorophyll a, suspended matter and yellow matter, among which the absorption coefficient of pure water molecules is a function of wavelength λ. Based on the measured chlorophyll a concentration, suspended matter concentration and absorption coefficient of yellow matter at 440nm at the sampling points in the study area, the total absorption coefficient of the study area can be obtained.

[0043] The calculation formula of the absorption coefficient of chlorophyll a is:

[0044]

[0045] a Chla (λ)=(a0(λ)+a1(λ)ln a Chla (440))×a Chla (440)

[0046] In the formula, C chla is the chlorophyll a concentration, a0(λ) and a1(λ) are empirical parameters, and a chl (440) is the spectral absorption coefficient of Taihu chlorophyll a at a wavelength of 440nm.

[0047] The calculation formula of the absorption coefficient of suspended matter is:

[0048]

[0049] In the formula, C S is the suspended matter concentration, a s (440) is the spectral absorption coefficient of Taihu suspended matter at a wavelength of 440 nm, is the normalized unit absorption coefficient of suspended matter.

[0050] The calculation formula for the absorption coefficient of yellow substances is:

[0051] a CDOM (λ) = a CDOM (440)exp[-S CDOM (λ-440)]

[0052] In the formula, S CDOM is the slope of the absorption spectrum of the yellow substance. The average value determined in this study based on the measured data is 0.015nm-1.

[0053] Since the backscattering effect of yellow matter on light is very weak, the calculation of the backscattering coefficient only considers three components in the water body, namely water molecules, chlorophyll and suspended matter, among which the scattering coefficient of pure water molecules is a function of wavelength λ.

[0054] Among them, the calculation formula of the scattering coefficient of chlorophyll a is:

[0055]

[0056] In the formula, is the normalized unit absorption coefficient of chlorophyll a, x is the exponential term of chlorophyll a concentration, and its optimization range is (0.01, 2.00) with a step size of 0.01.

[0057] The calculation formula of the scattering coefficient of suspended matter is:

[0058]

[0059] Where b s (550) is the scattering coefficient of the suspended matter at 550 nm, and its optimization range is set to (0.01, 2.00) with a step size of 0.01; n s is the slope of the suspended matter scattering spectrum, and its optimization range is set to (-1.00, 1.00) with a step size of 0.01.

[0060] x、n s , and b s (550) The intrinsic optical parameters of the three study areas are taken as unknowns, and the calculation is repeated repeatedly under the set initial value, range and step size to obtain the s , and b s (550) The values ​​of the total scattering coefficients of the corresponding sampling points.

[0061] (III) Remote sensing reflectivity simulation results and determination of intrinsic optical parameters

[0062] The root mean square error is calculated based on the absorption coefficient and scattering coefficient calculated in step (ii) and the remote sensing reflectance in the wavelength range of 400-700nm obtained by simulating the 2SeaColor model of the present invention and the measured remote sensing reflectance. When the root mean square error reaches the minimum, the corresponding x is determined to be 0.41, n s =-1.15, b s (550) is 0.03, which is the intrinsic optical parameter value suitable for the inversion of the three elements of water color in the study area. With wavelength as the horizontal axis and the simulated remote sensing reflectivity as the vertical axis, a curve of remote sensing reflectivity versus wavelength is drawn, and representative sampling point data is selected to form a graph, such as Figure 3 As shown;

[0063] The calculation formula of the root mean square error is:

[0064]

[0065] Where i is the number of remote sensing reflectance bands, is the simulated remote sensing reflectivity, is the measured remote sensing reflectivity.

Claims

1. A method for optimizing and simulating the inherent optical parameters of a second type of water body, the method comprising the following steps: Step 1) Select the study area and design the experimental scheme to measure the chlorophyll a concentration, suspended matter concentration, absorption coefficient of yellow matter at 440nm and water surface spectrum at the sampling point; a) Select the study area; b) Design the lake test plan based on the study area; c) Field measurement of water quality parameters; Step 2) calculating the absorption coefficient and backscattering coefficient of each sampling point; a) Calculate the absorption coefficient of each sampling point in the study area; b) Calculate the backscattering coefficient of each sampling point in the study area: a(λ)=a W (λ)+a Chla (λ)+a s (λ)+a CDOM (l) (1) In formula (1), a(λ) refers to the total absorption coefficient of water at wavelength λ; a W (λ) is the absorption coefficient of water molecules, a Chla (λ) is the chlorophyll a absorption coefficient, a s (λ) is the absorption coefficient of suspended matter, a CDOM (λ) is the absorption coefficient produced by the yellow substance, and the unit is per meter; b b (λ)=b w (λ)+b chla (λ)+b s (l) (2) In formula (2), b b (λ) refers to the total scattering coefficient of water at wavelength λ; b w (λ) water molecule scattering coefficient; b Chla (λ) is the scattering coefficient of chlorophyll a; b a (λ) is the scattering coefficient of suspended matter; Step 3) Use the 2SeaColor radiation transfer model to simulate the remote sensing reflectance of each sampling point; a) The exponential term x of the intrinsic optical parameters of the study area, the slope n of the suspended matter scattering spectrum, s , and the scattering coefficient b of the suspended matter at 550nm s The three elements are taken as unknown quantities; b) the three unknown quantities are set with reasonable initial values, ranges and step sizes, and the 2SeaColor model is used to continuously cycle to obtain the values ​​corresponding to each set of x, n s and b s Remote sensing reflectivity; The theoretical formula of 2SeaColor model is as follows: m w =cosθ s ′ In formula (3), is the directional hemispherical reflectivity of the semi-infinite medium; x is the total backscattering coefficient of the water body b b The ratio of the total absorption coefficient a; μ w Refers to the cosine value of the solar zenith angle under a flat water surface; if the solar zenith angle on the water surface is θ s , then θ′ s =arcsin(sinθ s / n w ), where n w is the refractive index of water, which is 1.33; R(0 - ) is the radiance reflectance under the horizontal plane; Q is the ratio of the radiance reflectance upward to the radiance reflectance downward, which is 3.25; R rs is the off-water reflectivity; Step 4) Calculate the root mean square difference between the simulated remote sensing reflectance and the remote sensing reflectance of the measured sampling points and determine the intrinsic optical parameter values ​​of the study area; a) Calculate the root mean square difference (RMSE) between the simulated remote sensing reflectance and the synchronously measured remote sensing reflectance at each sampling point; b) Find the minimum RMSE and determine the intrinsic optical parameter values ​​of the study area.

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