本文選題：高光譜遙感　切入點(diǎn)：土壤有機(jī)質(zhì)　出處：《山東農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：有機(jī)質(zhì)是土壤的重要物質(zhì)組成,對于作物生長,土地保水保肥以及陸地生態(tài)系統(tǒng)的正常運(yùn)行均有重要作用。傳統(tǒng)的土壤有機(jī)質(zhì)測定方法以采樣--化驗(yàn)為主要手段,雖然精度較高,但費(fèi)時耗力,不易實(shí)施。高光譜遙感可以精確捕捉常規(guī)遙感所觀測不到的地物細(xì)微的反射光譜信息,從而實(shí)現(xiàn)對地物的準(zhǔn)確識別與反演,對于作物長勢監(jiān)測、土地利用評價(jià)以及精準(zhǔn)農(nóng)業(yè)具有重要意義。本研究以山東省泰安市為研究區(qū),以采集的92個土樣的有機(jī)質(zhì)含量及其室外反射光譜為研究對象,基于土壤有機(jī)質(zhì)光譜反演中的灰色特性,利用灰色系統(tǒng)理論,建立了土壤有機(jī)質(zhì)高光譜灰色估測模型,對土壤有機(jī)質(zhì)含量進(jìn)行估測,通過與經(jīng)典估測方法進(jìn)行對比,驗(yàn)證了灰色估測模型的有效性。主要研究內(nèi)容及結(jié)論如下:(1)確定了泰安市潮棕壤有機(jī)質(zhì)敏感波段和特征因子利用平方根、倒數(shù)、對數(shù)、一階微分及其組合、包絡(luò)線去除等10種光譜變換技術(shù)對光譜進(jìn)行變換,通過原始及變換光譜與土壤有機(jī)質(zhì)含量的相關(guān)分析確定了有機(jī)質(zhì)敏感波段,并利用極大相關(guān)性原則提取了特征因子。結(jié)果表明,10種變換技術(shù)中,一階微分及對數(shù)倒數(shù)的一階微分變換技術(shù)能明顯提高有機(jī)質(zhì)與反射光譜在可見光與近紅外的相關(guān)性,而平方根、倒數(shù)、對數(shù)變換無益于提高反射光譜與土壤有機(jī)質(zhì)的相關(guān)性;有機(jī)質(zhì)的反射光譜特征主要位于可見光485~760nm波段以及近紅外1375~1382nm、2121~2133nm、2336-2347nm三個水分吸收波段附近;選取的5個特征因子分別位于原始光譜665nm處,一階微分光譜575nm和2341nm處以及對數(shù)倒數(shù)的一階微分1378nm和2128nm處,相關(guān)性均大于0.55。(2)建立了土壤有機(jī)質(zhì)高光譜灰色估測模型基于土壤有機(jī)質(zhì)估測中的灰色特性和光譜特征因子的非時間序列特性,借助灰色系統(tǒng)理論,利用特征因子與因變量的相關(guān)系數(shù)和特征因子的標(biāo)準(zhǔn)差構(gòu)造權(quán)重,對關(guān)聯(lián)度進(jìn)行改進(jìn),得到加權(quán)距離灰色關(guān)聯(lián)度和灰色加權(quán)關(guān)聯(lián)度;而后利用識別殘差建立殘差修正模型,得到兩種具有殘差修正的灰色關(guān)聯(lián)估測模型,并在此基礎(chǔ)上利用修正值殘差的標(biāo)準(zhǔn)差將點(diǎn)值估測拓展為區(qū)間估測;最后與多元線性回歸、BP神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)模型進(jìn)行了比較。結(jié)果表明,加權(quán)距離灰色關(guān)聯(lián)度和灰色加權(quán)關(guān)聯(lián)度均可用于土壤有機(jī)質(zhì)高光譜估測,殘差修正模型能有效提高估測精度,而區(qū)間估測能隱涵部分不確定性的影響,反映有機(jī)質(zhì)的動態(tài)變化特性;5種模型的點(diǎn)值估測中,具有殘差修正的灰色加權(quán)關(guān)聯(lián)度模型和具有殘差修正的加權(quán)距離灰色關(guān)聯(lián)度估測模型精度最高,平均相對誤差分別為6.79%、7.94%,其次是支持向量機(jī),平均相對誤差為12.94%,BP神經(jīng)網(wǎng)絡(luò)和多元線性回歸模型表現(xiàn)較差,平均相對誤差均高于14%,說明灰色關(guān)聯(lián)估測模型在土壤有機(jī)質(zhì)高光譜估測方面擁有很大潛力。
[Abstract]:Organic matter is an important material composition of soil, which plays an important role in crop growth, soil moisture and fertilizer conservation and the normal operation of terrestrial ecosystem.The traditional method of soil organic matter determination is sample-test. Although the precision is high, it is time-consuming and difficult to carry out.Hyperspectral remote sensing can accurately capture the subtle reflectance spectrum information of ground objects that can not be observed by conventional remote sensing, thus realizing the accurate identification and inversion of ground objects, which is of great significance for crop growth monitoring, land use evaluation and precision agriculture.Taking Taian City, Shandong Province as the research area, the content of organic matter and its outdoor reflectance spectrum of 92 soil samples were studied. Based on the grey characteristics of soil organic matter spectral inversion, the grey system theory was used.The hyperspectral grey estimation model of soil organic matter was established and the content of soil organic matter was estimated. The validity of the grey estimation model was verified by comparing it with the classical estimation method.The main contents and conclusions are as follows: (1) 10 spectral transformation techniques, such as square root, reciprocal, logarithm, first-order differential and their combination, and envelope removal, are used to transform the spectrum of organic matter sensitive bands and characteristic factors of Chao Brown soil in Taian City.The sensitive bands of soil organic matter were determined by correlation analysis of original and transformation spectra and soil organic matter content, and the characteristic factors were extracted by the principle of maximum correlation.The results show that the first-order differential transformation technique and the logarithmic differential transformation technique can obviously improve the correlation between organic matter and the reflected spectrum in visible light and near infrared, while square root, reciprocal.The five characteristic factors are located at the original 665nm, the first order differential spectrum at 575nm and 2341nm, and the logarithmic reciprocal of the first order differential 1378nm and 2128nm, respectively.The hyperspectral grey estimation model of soil organic matter was established. Based on the grey characteristics of soil organic matter estimation and the non-time series characteristics of spectral characteristic factors, the grey system theory was used to estimate soil organic matter.By using the correlation coefficient of characteristic factor and dependent variable and the standard deviation of characteristic factor to construct weight, the correlation degree is improved, and the weighted distance grey correlation degree and grey weighted correlation degree are obtained, and then the residual correction model is established by identifying residual error.Two grey correlation estimation models with residual correction are obtained, and on this basis, the point value estimation is extended to interval estimation by using the standard deviation of the modified residual value, and finally the multivariate linear regression BP neural network is used.The support vector machine model is compared.The results show that the weighted distance grey correlation degree and grey weighted correlation degree can be used to estimate soil organic matter hyperspectral. The residual correction model can effectively improve the accuracy of estimation, while the interval estimation can cover the effect of partial uncertainty.Among the five models reflecting the dynamic characteristics of organic matter, the grey weighted relational degree model with residual correction and the weighted distance grey relational degree model with residual correction have the highest accuracy.The average relative error was 6.79 and 7.94, followed by support vector machine. The average relative error was 12.94 BP neural network and multivariate linear regression model.The average relative error is higher than 14, indicating that the grey correlation estimation model has great potential in the hyperspectral estimation of soil organic matter.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S153.621

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