本文選題：玄武巖 + 粗面巖��； 參考：《沈陽農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：土壤有機(jī)質(zhì)是土壤中含碳的有機(jī)化合物,是土壤的化學(xué)性質(zhì)之一。目前,傳統(tǒng)的土壤有機(jī)質(zhì)含量測(cè)定方法主要有重鉻酸鉀-硫酸消化法、TOC分析儀法、元素分析儀法。雖然化學(xué)分析法測(cè)定精度較高,但室內(nèi)化學(xué)分析費(fèi)時(shí)費(fèi)力。近來年,隨著高光譜遙感技術(shù)的不斷發(fā)展和數(shù)學(xué)多變量統(tǒng)計(jì)算法的深入研究,高光譜遙感以其光譜分辨率高、信息豐富等優(yōu)勢(shì)在土壤屬性預(yù)測(cè)方面得到了較快的發(fā)展。通過室內(nèi)光譜儀獲取土壤樣品的反射光譜數(shù)據(jù)省時(shí)省力,通過建立的模型進(jìn)行估測(cè),可以實(shí)現(xiàn)土壤有機(jī)質(zhì)含量的快速測(cè)定。本研究采集東北地區(qū)分布的玄武巖質(zhì)和粗面巖質(zhì)火山碎屑物發(fā)育的土壤,對(duì)土壤有機(jī)質(zhì)含量進(jìn)行化學(xué)分析,并利用ASD FieldSpec4光譜儀獲取土壤樣品的高光譜數(shù)據(jù)。通過對(duì)原始光譜反射率進(jìn)行平滑、去噪處理后結(jié)合連續(xù)統(tǒng)去除法、微分法和倒數(shù)對(duì)數(shù)法提取土壤光譜特征,采用多元逐步線性回歸、偏最小二乘回歸、主成分回歸法建立兩種巖性火山碎屑物發(fā)育土壤的有機(jī)質(zhì)含量預(yù)測(cè)模型,并依據(jù)校正相關(guān)系數(shù)、預(yù)測(cè)相關(guān)系數(shù)、校正標(biāo)準(zhǔn)誤差、預(yù)測(cè)標(biāo)準(zhǔn)誤差、交叉驗(yàn)證均方差、預(yù)測(cè)均方差、預(yù)測(cè)偏倚差對(duì)建立的預(yù)測(cè)模型進(jìn)行比較,分別獲取兩種巖性火山碎屑物發(fā)育土壤的有機(jī)質(zhì)含量最優(yōu)預(yù)測(cè)模型。玄武巖質(zhì)火山碎屑物發(fā)育土壤的光譜曲線在400～1300nm范圍內(nèi)呈急劇增加趨勢(shì),在1440～1860 nm范圍內(nèi)緩慢增加;粗面巖質(zhì)火山碎屑物發(fā)育土壤的光譜曲線在400～750 nm范圍內(nèi)呈急劇增加趨勢(shì),在750～1380 nm范圍內(nèi)增長(zhǎng)緩慢,在1420～1880 nm范圍內(nèi)趨于平緩。玄武巖質(zhì)火山碎屑物發(fā)育土壤的有機(jī)質(zhì)光譜響應(yīng)位于500nm、800 nm;粗面巖質(zhì)火山碎屑物發(fā)育土壤的光譜響應(yīng)位于405 nm、465 nm、575 nm、1105 nm。原始光譜進(jìn)行一階微分、二階微分、反射率倒數(shù)對(duì)數(shù)、反射率倒數(shù)對(duì)數(shù)的一階微分和反射率倒數(shù)對(duì)數(shù)的二階微分處理后,分別與相應(yīng)的玄武巖質(zhì)和粗面巖質(zhì)火山碎屑物發(fā)育土壤的有機(jī)質(zhì)含量進(jìn)行相關(guān)性分析,相關(guān)性均表現(xiàn)為顯著增強(qiáng)。玄武巖質(zhì)火山碎屑物發(fā)育土壤,除反射率倒數(shù)對(duì)數(shù)二階微分外,最大相關(guān)系數(shù)的絕對(duì)值均在0.8以上,一階微分的最大相關(guān)系數(shù)為-0.8898;粗面巖質(zhì)火山碎屑物發(fā)育的土壤,一階微分、反射率倒數(shù)對(duì)數(shù)的一階微分和反射率倒數(shù)對(duì)數(shù)的二階微分的最大相關(guān)系的絕對(duì)值亦在0.8以上,反射率倒數(shù)對(duì)數(shù)的一階微分的最大相關(guān)系數(shù)為-0.9029。全譜范圍內(nèi),采用多元逐步線性回歸、偏最小二乘、主成分回歸三種方法建立的兩種巖性火山碎屑物發(fā)育土壤有機(jī)質(zhì)含量的預(yù)測(cè)模型,均得到了較好的預(yù)測(cè)結(jié)果。其中,玄武巖質(zhì)火山碎屑物發(fā)育土壤的有機(jī)質(zhì)含量最優(yōu)預(yù)測(cè)模型為基于光譜反射率倒數(shù)對(duì)數(shù)的一階微分建立的多元逐步線性回歸模型。模型自變量數(shù)為7,預(yù)測(cè)決定系數(shù)Rv2=0.9720,預(yù)測(cè)均方差RMSEP=2.0590,sig=-0.0030.01。粗面巖質(zhì)火山碎屑物發(fā)育土壤的有機(jī)質(zhì)含量最優(yōu)預(yù)測(cè)模型為基于光譜反射率倒數(shù)對(duì)數(shù)的一階微分建立的偏最小二乘回歸模型。模型自變量數(shù)Pc = 5,建模相關(guān)系數(shù)Rc = 0.9872,決定系數(shù)Rc2= 0.9745,建模均方根誤差RMSEC = 0.4821,校正偏差SEC = 0.4906,預(yù)測(cè)決定系數(shù)Rv2= 0.9702,預(yù)測(cè)均方根誤差RMSEP = 0.9563,校正偏差SEP = 0.9711,預(yù)測(cè)偏倚差Bias=-0.0637。
[Abstract]:Soil organic matter is an organic compound containing carbon in soil and one of the chemical properties of soil. At present, the main methods of determining the content of soil organic matter are potassium dichromate - sulfuric acid digestion, TOC analyzer and elemental analyzer. Although the precision of chemical analysis is high, the indoor chemical analysis is time-consuming and laborious. The continuous development of spectral remote sensing technology and the in-depth study of mathematical multivariable statistical algorithms. Hyperspectral remote sensing has developed rapidly in the prediction of soil properties with its high spectral resolution, rich information and so on. The number of reflectance spectra obtained by the indoor spectrometer is saved and estimated by the established model. A rapid determination of soil organic matter content can be achieved. In this study, the soils developed from basalt and coarse rock volcanic debris distributed in Northeast China were collected, and the content of soil organic matter was analyzed by chemical analysis. The hyperspectral data of soil samples were obtained by using ASD FieldSpec4 spectrometer. After de-noising, the spectral characteristics of soil are extracted with continuous division method, differential method and reciprocal logarithm method. Multiple stepwise linear regression, partial least squares regression and principal component regression method are used to predict the organic matter content of two types of lithologic volcaniclastic soil, and the correlation coefficients are corrected and the correlation coefficients are predicted and the calibration criteria are corrected. The error, the prediction standard error, the cross validation mean square deviation, the prediction mean square variance, the prediction bias difference and the comparison of the predicted models were made to obtain the optimal prediction model of the organic matter content of the two kinds of lithologic volcaniclastic soils. The spectral curves of the basalt volcaniclastic soil increased sharply in the range of 400 to 1300nm. The trend is increasing slowly within the range of 1440~1860 nm; the spectral curve of the developed soil with coarse rock volcanic debris increases rapidly within the range of 400~750 nm, and grows slowly within the range of 750~1380 nm, and tends to be gentle in the range of 1420~1880 nm. The spectral response of the organic matter of the basalt volcaniclastic soil is located in 500nm, 8 00 nm; the spectral response of the coarse rock volcaniclastic soil is located at 405 nm, 465 nm, 575 nm, and 1105 nm. for the first order differential, the two order differential, the reflectivity reciprocal logarithm, the first differential of the reflectivity reciprocal logarithm and the two order differential of the reflectivity inversion logarithm, respectively, and the corresponding basalt and rough rock volcanoes. The correlation between the organic matter content of the detrital soil and the correlation analysis shows that the correlation of the basalt volcanic debris developed soil, except the two order differential of the reflectivity inversion number, the absolute value of the maximum correlation coefficient is above 0.8, the maximum phase relation of the first order differential is -0.8898, and the development of the coarse rock volcanic debris is developed. The absolute value of the first order differential of the logarithm of the logarithm of the reflectivity reciprocal logarithm and the two order differential of the reflectivity reciprocal logarithm is also more than 0.8. The maximum correlation coefficient of the first order differential of the reflectivity reciprocal logarithm is within the -0.9029. total spectrum range, and the multiple stepwise linear regression, partial least squares, and principal component regression are used for three kinds of squares. The prediction model of the soil organic matter content of two types of lithologic volcanics developed by the method has been well predicted. Among them, the optimal prediction model of the organic matter content in the developing soil of basalt pyroclastic is a stepwise linear regression model based on the first order differential of the reciprocal logarithm of spectral reflectance. The number of variables is 7, the prediction coefficient Rv2=0.9720, the predicted mean square variance RMSEP=2.0590, the optimal prediction model for the organic matter content in the developing soil of sig=-0.0030.01. rough rock volcaniclastic is based on the partial least squares regression model based on the first order differential of the inverse logarithm of spectral reflectance. The number of model independent variables is Pc = 5, and the correlation coefficient Rc = 0. of the modeling is Rc = 0.. 9872, the determination coefficient Rc2= 0.9745, the square root mean square error RMSEC = 0.4821, the correction deviation SEC = 0.4906, the prediction coefficient Rv2= 0.9702, the mean square root error RMSEP = 0.9563, the correction deviation SEP = 0.9711, the prediction bias difference Bias=-0.0637.
【學(xué)位授予單位】：沈陽農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S153.6
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本文編號(hào)：2004305