本文選題：東北粳稻　切入點(diǎn)：高光譜遙感　出處：《沈陽(yáng)農(nóng)業(yè)大學(xué)》2017年博士論文

【摘要】：當(dāng)前我國(guó)東北水稻生產(chǎn)過(guò)程中化肥施用量連年增加,在一定程度上造成了部分土地退化、環(huán)境污染、病蟲害增加等多個(gè)生態(tài)問(wèn)題。因此在水稻生長(zhǎng)過(guò)程中對(duì)其生長(zhǎng)信息進(jìn)行快速、無(wú)損檢測(cè),對(duì)于輔助開(kāi)展水稻營(yíng)養(yǎng)診斷、精準(zhǔn)施肥提高化肥利用率,降低環(huán)境污染等具有重要意義。目前,由于空間分辨率和光譜信息的限制基于衛(wèi)星遙感和地面遙感建立的水稻生長(zhǎng)信息反演模型,難以符合區(qū)域級(jí)水稻生長(zhǎng)信息精準(zhǔn)反演的需求。隨著無(wú)人機(jī)和高光譜技術(shù)的快速發(fā)展,為解決區(qū)域級(jí)水稻生長(zhǎng)信息精準(zhǔn)反演提供了新的方法和技術(shù)支撐。本研究以東北粳稻為主要研究對(duì)象,于2015年至2016年連續(xù)兩年在沈陽(yáng)農(nóng)業(yè)大學(xué)道南試驗(yàn)田開(kāi)展基于"3414"肥料設(shè)計(jì)的水稻田間栽培試驗(yàn)。集成無(wú)人機(jī)高光譜遙感平臺(tái),在水稻不同生長(zhǎng)期內(nèi)獲取水稻冠層高光譜遙感影像,采用光譜角填圖法、最大似然分類法、Fisher判別法、支持向量機(jī)法、第二代小波融合算法等提取水稻特征信息并分類,通過(guò)分類結(jié)果可知,采用基于期望最大算法優(yōu)化的二代小波分類法提取的純凈水稻高光譜信息分類精度為90.36%,高于其他分類算法提取的水稻高光譜信息。分類結(jié)果表明,采用二代小波分類算法對(duì)于復(fù)雜的稻田環(huán)境能夠比較理想的提取純凈的水稻高光譜信息。通過(guò)分析東北粳稻的高光譜特性可知,在400nm～750nm的可見(jiàn)光區(qū)域,水稻高光譜主要是由葉片內(nèi)色素含量決定光譜信息的變化,水稻對(duì)藍(lán)光波段和紅光波段的吸收性較好,而對(duì)綠光波段吸收弱于藍(lán)光和紅光波段,在750nm～1000nm的近紅外范圍內(nèi)水稻葉片光譜特征主要受細(xì)胞結(jié)構(gòu)變化的影響。在近紅外波段水稻對(duì)光的反射性較強(qiáng),而吸收性較弱。通過(guò)分析葉片高光譜特性可知,水稻正面與背面的反射率變化趨勢(shì)是一致的,在可見(jiàn)光區(qū)域并無(wú)明顯差異,在近紅外區(qū)域內(nèi),水稻正面反射率要稍稍大于背面反射率。鮮葉與干葉比較,鮮葉反射率整體上明顯小于干葉的反射率,水稻生長(zhǎng)含量不同所引起光譜信息的變化是本研究基于高光譜信息反演水稻生長(zhǎng)信息的重要理論基礎(chǔ)。利用多光譜植被指數(shù)和高光譜特征信息采用回歸分析的方法建立水稻葉綠素、氮素、葉面積指數(shù)(LAI)、生物量等水稻生長(zhǎng)信息反演模型,模型反演結(jié)果表明,基于多光譜植被指數(shù)的葉綠素、鮮生物量、干生物量、LAI、氮素反演模型的決定系數(shù)分別為0.498、0.485、0.414、0.599、0.542�；诟吖庾V特征信息的葉綠素、鮮生物量、干生物量、LAI、氮素反演模型的決定系數(shù)分別為0.617、0.569、0.615、0.690、0.668。高光譜特性信息反演的水稻生長(zhǎng)信息模型效果要優(yōu)于通過(guò)傳統(tǒng)多光譜建立的水稻生長(zhǎng)信息反演模型,但通過(guò)回歸分析方法建立的水稻生長(zhǎng)信息反演模型容易受到外界因素的干擾。本研究通過(guò)優(yōu)化作物冠層輻射傳輸機(jī)理模型PROSAIL,提出N-PROSAIL水稻生長(zhǎng)信息反演模型,相比現(xiàn)有的水稻生長(zhǎng)信息PROSAIL反演模型,N-PROSAIL模型能夠彌補(bǔ)現(xiàn)有PROSAIL模型無(wú)法反演水稻氮素的不足。在此基礎(chǔ)上,分別利用查找表法和數(shù)值優(yōu)化的方法反演水稻生長(zhǎng)信息。采用N-PROSAIL模型反演水稻生長(zhǎng)信息的模型決定系數(shù)為葉綠素0.712、鮮生物量0.565、干生物量0.696、LAI0.696、氮素0.709。模型的反演效果要優(yōu)于采用回歸分析的反演精度。采用遺傳神經(jīng)網(wǎng)絡(luò)、高斯過(guò)程回歸、核嶺回歸、隨機(jī)森林四種機(jī)器學(xué)習(xí)算法建立水稻生長(zhǎng)信息反演模型。其中本研究采用高斯徑向基核函數(shù)對(duì)嶺回歸算法進(jìn)行核化,轉(zhuǎn)換為核嶺回歸算法,在降低模型輸入?yún)?shù)的同時(shí),還能夠比較理想的處理水稻高光譜這種非線性的問(wèn)題。本研究利用核嶺回歸反演水稻生長(zhǎng)信息模型的精度要優(yōu)于其他三種機(jī)器學(xué)習(xí)算法,并且鮮生物量和葉面積指數(shù)的模型決定系數(shù)分別為0.723和0.786,高于其他方法建立的反演模型。本研究通過(guò)無(wú)人機(jī)高光譜遙感平臺(tái)獲取東北粳稻冠層高光譜信息,建立高光譜信息與水稻生長(zhǎng)之間的反演模型及研究結(jié)果能夠?yàn)樗旧L(zhǎng)信息快速、無(wú)損檢測(cè)、科學(xué)施肥提供一定的理論基礎(chǔ)和技術(shù)支撐。
[Abstract]:The amount of chemical fertilizer in Northeast of rice in China in the process of production has increased year by year, resulting in a part of land degradation, environmental pollution in a certain extent, pests and diseases increased many ecological problems. So in the process of rice growth in the growth of information fast, nondestructive testing, to carry out auxiliary rice nutritional diagnosis, improve the utilization rate of fertilizer fertilizer plays an important role in reducing environmental pollution. At present, due to the limited spatial resolution and spectral information based on growth model of information retrieval of satellite remote sensing and ground remote sensing of rice, rice growth is difficult to meet the region level accurate information inversion needs. With the rapid development of UAV and hyperspectral technology, provides methods and techniques support new growth information accurate inversion to solve regional level. In this study, the Northeast japonica rice as the main object of study, from 2015 to 2016 for two consecutive years in the Shenyang Agricultural Uinversity Daonan experimental field development based on the "3414" Fertilizer Design of rice field cultivation experiment. Integrated UAV hyperspectral remote sensing platform, obtaining the rice canopy hyperspectral remote sensing image in different rice growth period, using spectral angle mapping method, maximum likelihood classification, Fisher discriminant analysis, support vector machine algorithm, feature extraction of rice information fusion and classification of the second Dai Xiaobo, through the classification results, the classification accuracy of Hyperspectral Information Extraction of two pure rice Dai Xiaobo classification expectation maximization algorithm based on Optimization of rice was 90.36%, higher than other high spectral information classification algorithm. The classification results show that the two Dai Xiaobo classification algorithm for extraction of complex environment to paddy field the ideal of pure rice high spectral information. Through the analysis of spectral characteristics of japonica, 400nm ~ 750nm visible Light area, rice high spectrum is dominated by changes in leaf pigment content determines the spectral information, rice absorption of the blue band and red band better, and to the green light absorption weak blue and red band effect in the near infrared range 750nm ~ 1000nm within the cell structure is mainly affected by the changes of the rice Ye Pianguang spectrum characteristics in. Near infrared light reflection on rice is strong, and weak absorption. By analyzing the spectral characteristics of the blade, the front and back of the rice albedo variation trend is consistent, no apparent difference in the visible light region, in the near infrared region, rice positive reflectivity to be slightly larger than the back reflectivity of fresh leaves and dry leaves compared. On the whole, fresh leaf reflectance was smaller than the dry leaf reflectance of rice growth in different caused by the changes of the spectrum information is the research of hyperspectral data inversion based on Rice An important theoretical basis for long information. Using multi spectral vegetation index and hyperspectral characteristics of information to establish the chlorophyll, by using the method of regression analysis of nitrogen, leaf area index (LAI), the biomass of rice growth information retrieval model, model inversion results show that the multi spectral vegetation index based on chlorophyll, biomass, stem biomass the amount of LAI, the coefficient of determination of nitrogen, the inversion model respectively based on 0.498,0.485,0.414,0.599,0.542. Hyperspectral Feature Information of chlorophyll, biomass, stem biomass, LAI, nitrogen determination coefficient inversion model respectively. The growth model of information 0.617,0.569,0.615,0.690,0.668. inversion of hyperspectral characteristic information retrieval rice growth information model is better than established by traditional multispectral rice but, through the regression analysis method to establish the rice growth information retrieval model is vulnerable to interference from external factors. This research The PROSAIL through the optimization of crop canopy radiative transfer mechanism model, N-PROSAIL information retrieval model is proposed for rice growth, compared with the existing rice growth information PROSAIL inversion model, to supplement the existing PROSAIL model can not inversion of nitrogen in rice N-PROSAIL model. On this basis, respectively, using look-up table method and the method of numerical optimization inversion of rice growth growth information. Using N-PROSAIL model inversion rice model decision coefficient 0.712 for chlorophyll, 0.565 fresh biomass, stem biomass of 0.696, LAI0.696, 0.709. model of nitrogen inversion effect is superior to the inversion accuracy of regression analysis. Using genetic neural network, Gauss regression, kernel ridge regression, random forest four machine learning algorithm of rice growth the information retrieval model. Which were used in the study of kernel ridge regression algorithm Gauss radial basis kernel function conversion The kernel ridge regression algorithm, reduce the input parameters of the model at the same time, the nonlinear processing of rice in high spectrum of this can also be an ideal problem. This study uses kernel ridge regression inversion rice growth information model is more accurate than other three kinds of machine learning algorithm, and the model decision coefficient and leaf area index of fresh biomass was 0.723 and 0.786, higher than the other inversion model established method. Through the study of UAV hyperspectral remote sensing platform for the northeast rice canopy spectral information, the establishment of the high spectral information between rice growth model and inversion results can for rice growth information fast, nondestructive testing, and provide a certain theoretical foundation and technical support of scientific fertilization.

【學(xué)位授予單位】：沈陽(yáng)農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：S511.22;S127

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