發(fā)布時間：2018-04-29 18:04

  本文選題：冬小麥 + 長勢　； 參考：《山西農(nóng)業(yè)大學(xué)》2016年博士論文

【摘要】：冬小麥?zhǔn)俏覈�的第三大糧食作物,實(shí)時、準(zhǔn)確、快速掌握冬小麥長勢狀況對于施肥、灌溉等田間管理和實(shí)現(xiàn)產(chǎn)量與品質(zhì)的調(diào)控具有重要作用。基于高光譜遙感技術(shù)已經(jīng)廣泛的應(yīng)用于作物研究。作物光譜信息提取不準(zhǔn)確、監(jiān)測模型精度和穩(wěn)健度不高是限制光譜遙感技術(shù)進(jìn)一步應(yīng)用的主要原因。本研究以不同年份、不同冬小麥品種、不同氮肥水平的田間小區(qū)試驗(yàn)為基礎(chǔ),綜合利用光譜技術(shù)、多元統(tǒng)計分析和化學(xué)計量學(xué)方法,基于高光譜遙感技術(shù)對冬小麥長勢監(jiān)測展開了研究,得出以下主要結(jié)論：1本研究所獲取的冬小麥長勢數(shù)據(jù)符合冬小麥一般生長規(guī)律,且差異性較大,能夠表征不同生長條件下的冬小麥長勢狀況,本文基于因子分析構(gòu)建的綜合長勢指標(biāo)(Comprehensive growth indicators, CGI)能夠表征冬小麥長勢狀況;2歸一化植被指數(shù)(Normalized difference vegetation index, NDVI)與葉面積指數(shù)(Leaf area index, LAI)、地上生物量(Above ground biomass, AGB)存在“飽和”現(xiàn)象,研究發(fā)現(xiàn)該現(xiàn)象是由構(gòu)成NDVI的波段先發(fā)生“飽和”造成的,證明發(fā)生“光譜飽和”現(xiàn)象的閾值為：LAI2.5或AGB 1 kg m-2。同時發(fā)現(xiàn)某些植被指數(shù)在高LAI和AGB條件下可以克服“飽和”現(xiàn)象,通過分析這些植被指數(shù)的構(gòu)成總結(jié)出克服“光譜飽和”現(xiàn)象的方法：(1)構(gòu)成該植被指數(shù)的波段應(yīng)盡可能多的集中在“紅邊”區(qū)域；(2)該植被指數(shù)的取值范圍應(yīng)不受限制;(3)準(zhǔn)確挖掘光譜特征信息,并利用多元統(tǒng)計和化學(xué)計量學(xué)方法構(gòu)建穩(wěn)健性的監(jiān)測模型;3平滑處理中的9點(diǎn)平滑處理(SM9)、變化處理中的平方根(T4)處理和校正處理中的噪音校正(Noise)為最佳的光譜數(shù)據(jù)預(yù)處理方法；4光譜波段：400、512、536、555、680、700、735、760、816、890、920、1130、2040和2430 nm與冬小麥長勢密切相關(guān);5非線性模型決策支持機(jī)(Support vector machine, SVM)要優(yōu)于偏最小二乘(Partial least square regression, PLSR)線性模型,主成分回歸(Principle component regression, PCR)線性模型最差；在各化學(xué)計量學(xué)模型中基于CGI的表現(xiàn)最好,表明利用CGI表征冬小麥長勢是有意義的；研究證實(shí)基于SVM非線性模型具有一定的穩(wěn)健性和普適性,但是在監(jiān)測冬小麥綜合長勢指標(biāo)時,基于T4處理的PLSR模型(Rc2=0.768,RPDc=1.973;Rv2=0.724, RPDv=1.693)要優(yōu)于SVM模型(Rc2=0.813, RPDC=1.945; Rv2=0.715, RPDV=1.554);基于特征波段建立的冬小麥長勢指標(biāo)的PLSR-SMLR模型具有一定的應(yīng)用潛力;除PCR模型和PLR-SMLR模型中的PWC的原始光譜(SM0)模型表現(xiàn)較好外,所有基于原始光譜的冬小麥長勢指標(biāo)的監(jiān)測模型表現(xiàn)最差,表明光譜數(shù)據(jù)經(jīng)預(yù)處理后可以提高冬小麥長勢指標(biāo)監(jiān)測模型的表現(xiàn)。其中,原始光譜的平方根處理(T4)的適用性要優(yōu)于其它預(yù)處理方法6利用冬小麥高光譜技術(shù)監(jiān)測冬小麥長勢的光譜處理流程為：光譜數(shù)據(jù)的平方根處理→冬小麥長勢指標(biāo)和預(yù)處理光譜的相關(guān)性分析→結(jié)合PLSR和SMLR提取冬小麥長勢的光譜特征信息,并利用相關(guān)性分析結(jié)果和多元統(tǒng)計分析結(jié)果進(jìn)行多方面、多角度驗(yàn)證→利用PLSR-SMLR、PLSR線性方法或SVM方法構(gòu)建冬小麥長勢的預(yù)測模型→綜合對比分析,選擇預(yù)測精度高、表現(xiàn)穩(wěn)定的模型。
[Abstract]:Winter wheat is the third largest grain crop in China. Real-time, accurate, fast mastery of winter wheat growth condition plays an important role in fertilization, irrigation, field management and control of yield and quality. Based on hyperspectral remote sensing technology, it has been widely used in crop research. Low health is the main reason for limiting the further application of spectral remote sensing technology. Based on the field plot experiments of different winter wheat varieties and different nitrogen fertilizer levels, this study is based on the comprehensive utilization of spectral, multivariate statistical analysis and chemometrics methods, based on hyperspectral remote sensing technology to study the monitoring of winter wheat growth. The main conclusions are as follows: 1 the winter wheat growth data obtained by the research institute are in accordance with the general growth pattern of winter wheat, and the difference is large, which can characterize the growth condition of Winter Wheat under different growth conditions. The comprehensive growth index (Comprehensive growth indicators, CGI) based on factor analysis can characterize the long winter wheat length. The 2 normalized vegetation index (Normalized difference vegetation index, NDVI) and the leaf area index (Leaf area index, LAI). The aboveground biomass (Above ground biomass) has a "saturation" phenomenon. It is found that the phenomenon is first "saturated", which proves that the phenomenon of "spectral saturation" occurs. The threshold is: LAI2.5 or AGB 1 kg m-2. also found that some vegetation indices can overcome the "saturation" phenomenon under the conditions of high LAI and AGB. By analyzing the composition of these vegetation indices, the method of overcoming "spectral saturation" is summed up: (1) the band of the vegetation index should be concentrated in the red edge area as much as possible; (2) The range of vegetation index should be unrestricted; (3) accurate mining of spectral feature information, and using multivariate statistical and chemometrics methods to build a robust monitoring model; 9 point smoothing treatment (SM9) in 3 smoothing treatment, noise correction (Noise) in the square root (T4) treatment and correction processing (Noise) as the best spectral data preconditioning The 4 spectral bands, 40051253655568070073576081689092011302040 and 2430 nm, are closely related to the winter wheat growth, and the 5 nonlinear model decision support machine (Support vector machine, SVM) is superior to the linear model of partial least squares (Partial least square regression, PLSR), principal component regression (Principle) The linear model of gression, PCR) is the worst, and the performance of CGI based on the chemometrics model is the best, indicating that it is meaningful to use CGI to characterize the winter wheat growth. The research confirms that the SVM nonlinear model has certain robustness and universality, but the PLSR model based on T4 treatment (Rc2=0.76) is based on the monitoring of the comprehensive growth index of winter wheat. 8, RPDc=1.973, Rv2=0.724, RPDv=1.693) are superior to the SVM model (Rc2=0.813, RPDC=1.945; Rv2=0.715, RPDV=1.554), and the PLSR-SMLR model based on the characteristics of the winter wheat growth index based on characteristic bands has a certain application potential; the original spectrum of the PWC is better than the PCR model and PLR-SMLR model, all based on the original spectrum. The monitoring model of winter wheat growth index is the worst, which indicates that the spectral data can improve the performance of the monitoring model of winter wheat growth index after pretreatment. The applicability of the square root treatment (T4) of the original spectrum is better than that of other pretreatment methods 6 using the winter wheat high light spectrum technology to monitor the winter wheat growth. The square root processing of the spectral data, the correlation analysis of the winter wheat growth index and the preprocessing spectrum, and the extraction of the spectral characteristics of the winter wheat growth with PLSR and SMLR, and using the correlation analysis results and multivariate statistical analysis results to carry out multiple aspects, multi angle verification, PLSR-SMLR, PLSR linear method or SVM method construction Prediction model of winter wheat growth > comprehensive comparative analysis, choose a model with high prediction accuracy and stable performance.

【學(xué)位授予單位】：山西農(nóng)業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：S512.11

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