本文選題：渾濁水體 + 固有光學(xué)量��； 參考：《南京師范大學(xué)》2015年碩士論文

【摘要】：本文面向渾濁富營(yíng)養(yǎng)化二類水體,以太湖為研究區(qū),在QAA基礎(chǔ)上構(gòu)建了水體吸收系數(shù)和后向散射系數(shù)雙波段(QAA-DB)反演算法以及去純水以外的吸收系數(shù)aexp(λ)分解算法。(1)QAA-DB算法在對(duì)水體反射光譜特征研究的基礎(chǔ)上,首先利用677nm吸收谷和701nnm反射峰之間反射率的光譜斜率(Slope)為分類指標(biāo),以Slope=0.32為臨界值,將混濁水體分為兩類具有不同光學(xué)特征的水體,針對(duì)兩類水體,分別構(gòu)建IOPs反演模型,針對(duì)不同類別水體設(shè)置不同的模型參數(shù),對(duì)于顆粒物后向散射系數(shù)進(jìn)行分段模擬。1)400-685nm波段采用冪函數(shù)形式模擬,基于QAA算法,同時(shí)選取550nm和675nm兩個(gè)參考波長(zhǎng)進(jìn)行顆粒物后向散射系數(shù)冪函數(shù)外推,并基于數(shù)據(jù)同化思想,利用多模型協(xié)同反演策略,對(duì)外推結(jié)果進(jìn)行加權(quán)優(yōu)化,確定最優(yōu)顆粒物后向散射系數(shù)；2)對(duì)于大于685nm波段的顆粒物后向散射系數(shù),以定值進(jìn)行模擬；最后,通過(guò)生物光學(xué)模型,計(jì)算得到吸收系數(shù)。驗(yàn)證數(shù)據(jù)表明,QAA-DB算法對(duì)于復(fù)雜二類水體吸收系數(shù)反演誤差MAPE為19.71%,RMSE為1.3933,反演精度令人滿意。(2)針對(duì)內(nèi)陸水體吸收系數(shù)的分解,本文提出了兩種改進(jìn)算法,即基于經(jīng)驗(yàn)比值的吸收系數(shù)分解算法和基于高斯函數(shù)參數(shù)化的吸收系數(shù)分解算法,將aexp(λ)進(jìn)行分解,得到浮游植物吸收系數(shù)αρh(λ)、非色素顆粒物及CDOM吸收光譜之和adm (λ)、1)基于經(jīng)驗(yàn)比值的吸收系數(shù)分解算法,首先經(jīng)驗(yàn)確定各組分吸收系數(shù)在450和480nm的比值及αdm(λ)衰減常數(shù)S,通過(guò)聯(lián)立方程并結(jié)合αdm(λ)光譜負(fù)指數(shù)外推參數(shù)化模型,求解整個(gè)波段上的adm (λ),進(jìn)而求得aρh(λ),驗(yàn)證數(shù)據(jù)結(jié)果表明,最終adm (λ)分解誤差MAPE為24.72%,RMSE為0.81；2)基于高斯函數(shù)參數(shù)化的吸收系數(shù)分解算法,利用12個(gè)高斯函數(shù)對(duì)apn(λ)進(jìn)行參數(shù)化,利用負(fù)指數(shù)模型參數(shù)化adm (λ),并經(jīng)驗(yàn)確定adm(440),在此基礎(chǔ)上,通過(guò)最小二乘優(yōu)化算法進(jìn)行未知參數(shù)的求解,實(shí)現(xiàn)對(duì)吸收系數(shù)的分解,最終adm(λ)分解誤差M.APE為27.73%,RMSE為1.02。對(duì)于復(fù)雜二類水體,本文改進(jìn)的兩種分解算法都成功地對(duì)aexp(λ)進(jìn)行了分解,精度令人滿意。
[Abstract]:This paper aims at the turbid eutrophication of the second kind of water bodies, taking Taihu Lake as the research area. On the basis of QAA, the inversion algorithm of water absorption coefficient and backscattering coefficient is constructed, and the decomposition algorithm of absorption coefficient aexpA (位) except pure water is constructed. Firstly, the spectral slope of reflectivity between the 677nm absorption valley and the reflection peak of 701nnm is used as the classification index, and the critical value of Slope=0.32 is taken as the critical value. The turbid water body is divided into two kinds of water bodies with different optical characteristics. For the two kinds of water bodies, the IOPs inversion model is constructed separately. According to the different model parameters of different water bodies, the backscattering coefficients of particles are simulated by power function in the wavelength of 400-685 nm, based on the QAA algorithm. At the same time, two reference wavelengths, 550nm and 675nm, are selected to extrapolate the backscattering coefficients of particles. Based on the idea of data assimilation, the weighted optimization of the extrapolation results is carried out by using multi-model cooperative inversion strategy. The optimum backscattering coefficient of particles is determined to be 2) the backscattering coefficient of particles larger than 685nm band is simulated by the fixed value. Finally, the absorption coefficient is calculated by the bio-optical model. The verification data show that the inversion error of the QAA-DB algorithm for the absorption coefficients of complex water bodies is 19.71% (MAPE = 1.3933, and the inversion accuracy is satisfactory. 2) in view of the decomposition of the absorption coefficients of inland water bodies, two improved algorithms are proposed in this paper. That is, the absorption coefficient decomposition algorithm based on the empirical ratio and the absorption coefficient decomposition algorithm based on the parameterization of the Gao Si function are used to decompose expp (位). A decomposition algorithm of phytoplankton absorption coefficient 偽 蟻 h (位 ~ (1), sum of absorption spectra of non-pigmented particles and CDOM) based on empirical ratio was obtained. First, the ratio of absorption coefficient to 480nm and the attenuation constant of 偽 dm (位) are determined empirically. By means of simultaneous equation and a dm (位) spectral negative exponent extrapolation parameterized model, the adm (位) on the whole band is solved, and then a 蟻 h (位) is obtained. Finally, adm (位) decomposition error MAPE is 24.72. Based on the Gao Si function parameterized absorption coefficient decomposition algorithm, 12 Gao Si functions are used to parameterize APN (位), and negative exponent model is used to parameterize adm (位). Using the least square optimization algorithm to solve the unknown parameters, the absorption coefficient is decomposed. Finally, the decomposition error of ADM (位) M.APE is 27.73 and RMSE is 1.02. For complex two kinds of water bodies, the two improved decomposition algorithms in this paper have successfully decomposed expp (位) with satisfactory accuracy.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：X87;P332

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本文編號(hào)：1937994