本文關(guān)鍵詞：基于遙感數(shù)據(jù)的低山丘陵區(qū)蘋果樹冠層葉綠素含量反演　出處：《山東農(nóng)業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Sentinel-2A Minnaert模型 混合像元分解 植被指數(shù) BP神經(jīng)網(wǎng)絡(luò) 支持向量機(jī)回歸

【摘要】：葉綠素是作物進(jìn)行光合作用的主要載體,是檢測作物光合作用能力和生長發(fā)育狀況的重要指標(biāo)。傳統(tǒng)的葉綠素含量實驗室化學(xué)測定方法,費時、費力,不利于大面積監(jiān)控作物長勢狀況。而近年來快速發(fā)展的遙感技術(shù)以其監(jiān)測速度快、成本低、面積大等優(yōu)點,為作物葉綠素含量的反演提供了一種新的技術(shù)方法。因此,基于遙感技術(shù)反演作物葉綠素含量具有重要的理論與現(xiàn)實意義。本研究以“蘋果之都”之稱的山東省棲霞市為研究區(qū),以Sentinel-2A遙感影像和近地面實測的蘋果樹冠層高光譜數(shù)據(jù)為數(shù)據(jù)源,遙感反演蘋果樹冠層葉綠素含量。首先,利用余弦校正和Minnaert模型,對大氣校正后的遙感影像進(jìn)行地形輻射校正;在結(jié)合近地面實測蘋果樹冠層高光譜數(shù)據(jù)進(jìn)行混合像元分解的基礎(chǔ)上,進(jìn)行蘋果樹冠層反射率反演;然后,在借鑒前人已構(gòu)建的植被指數(shù)的基礎(chǔ)上,以Sentinel-2A影像的藍(lán)光、綠光、紅光、紅邊與近紅外波段數(shù)據(jù)構(gòu)建的植被指數(shù),篩選蘋果樹冠層葉綠素植被指數(shù);最后,基于植被指數(shù)構(gòu)建蘋果樹冠層葉綠素含量的反演模型并進(jìn)行檢驗,對比分析多種模型的精度,優(yōu)選出最佳的反演模型。主要研究結(jié)果如下:(1)進(jìn)行了蘋果樹冠層反射率反演及精度分析對Sentinel-2A多光譜遙感影像進(jìn)行了大氣校正,在此基礎(chǔ)上,使用余弦校正和Minnaert模型對研究區(qū)進(jìn)行了地形輻射校正。其中,Minnaert模型校正后,影像的均值和標(biāo)準(zhǔn)差均小于余弦校正后影像,其影像的均值接近于大氣校正影像的均值,很好地去除了地形陰影,降低了陰陽坡對比度,消除或減弱了地形的影響,得到了地表反演反射率。結(jié)合近地面實測數(shù)據(jù),利用線性模型對地表反演反射率影像進(jìn)行了混合像元分解,得到了蘋果樹冠層的反演反射率。通過對影像進(jìn)行處理,表觀反射率、地表反演反射率、冠層反演反射率與冠層實測反射率的相對誤差是逐步降低的。冠層反演反射率的數(shù)值和冠層實測反射率的值是最為相近的,波段2~8A的相對誤差為14.4%、14.6%、9.5%、10.1%、1.6%、0.4%、1.4%和2.0%,說明通過各種影像處理得到了更加真實的冠層光譜,為后續(xù)分析提供了精度保證。(2)構(gòu)建及篩選了蘋果樹冠層葉綠素植被指數(shù)通過綜合考慮綠色植被的光譜特性及Sentinel-2A影像的波段,借鑒RVI、CI、NDVI的構(gòu)造原理及形式,以Sentinel-2A的藍(lán)光波段2、綠光波段3、紅光波段4、紅邊波段7、近紅外波段8和近紅外波段8A構(gòu)建了12種植被指數(shù),通過與葉綠素含量進(jìn)行相關(guān)性分析,并對植被指數(shù)進(jìn)行自相關(guān)性分析,優(yōu)選出了3個植被指數(shù)系列,為系列1(RVIblue+RVIred+RVIre)、系列2(CIblue+CIred+CIre)和系列3(NDVIgreen+NDVIred+NDVIre)。(3)建立與驗證了蘋果樹冠層葉綠素含量反演模型以植被指數(shù)系列1、系列2和系列3分別為自變量,蘋果樹冠層葉綠素含量為因變量,建立了BP神經(jīng)網(wǎng)絡(luò)反演模型和支持向量機(jī)回歸反演模型。以NDVIgreen+NDVIred+NDVIre植被指數(shù)建立的BP神經(jīng)網(wǎng)絡(luò)反演模型3的建模及檢驗的決定系數(shù)(Rc2=0.674,Rv2=0.601)均大于BP神經(jīng)網(wǎng)絡(luò)反演模型1和模型2,均方根誤差(RMSEc=0.169,RMSEv=0.185)都小于模型1和模型2,反演效果比較好。以NDVIgreen+NDVIred+NDVIre植被指數(shù)建立的支持向量機(jī)回歸反演模型3的建模及檢驗的決定系數(shù)均大于支持向量機(jī)回歸反演模型1和模型2,分別為0.729和0.667,均方根誤差都小于模型1和模型2,分別為0.159和0.178,反演效果比較好。支持向量機(jī)回歸反演模型3優(yōu)于BP神經(jīng)網(wǎng)絡(luò)反演模型3,表明支持向量機(jī)回歸反演模型3效果最佳,可以很好地反演蘋果冠層葉綠素含量,也表明Sentinel-2A影像在冠層葉綠素反演中的有效性。綜上所述,Sentinel-2A遙感影像結(jié)合近地面高光譜測定數(shù)據(jù),為低山丘陵區(qū)蘋果樹冠層葉綠素含量的宏觀監(jiān)測與快速診斷提供了新的方法,為農(nóng)業(yè)信息化的發(fā)展提供了理論依據(jù)和技術(shù)支撐。
[Abstract]:Chlorophyll is the main carrier for crop photosynthesis, is an important indicator of the ability to detect crop photosynthesis and growth status. The chlorophyll content of laboratory chemical measuring method, the traditional time-consuming, laborious, not conducive to large-scale monitoring of crop growth status. The remote sensing technology in recent years the rapid development of the monitoring of fast speed, low cost, the advantages of the area so, it provides a new technical method for the inversion of crop chlorophyll content. Therefore, the chlorophyll content of crops based on remote sensing technology has important theoretical and practical significance. Based on the "Apple Capital" of the Shandong city of Qixia Province as the study area, the apple tree canopy hyperspectral remote sensing image data Sentinel-2A and near ground data as the data source, the remote sensing inversion of Apple Tree Canopy Chlorophyll content. Firstly, using cosine correction and Minnaert model, after atmospheric correction. The sense of image topographic correction; spectral unmixing based on the combination of near ground measurement of apple tree canopy hyperspectral data, of apple tree canopy reflectance inversion; then, on the basis of previous vegetation index has been constructed on the Sentinel-2A image of the blue, green, red, red and near-infrared vegetation index band data construction and screening of Apple Tree Canopy Chlorophyll vegetation index; finally, test the inversion model of vegetation index of chlorophyll content in apple tree canopy and based on the comparative analysis of various model precision, select the best inversion model. The main results are as follows: (1) the inversion and accuracy analysis of the reflectance of apple tree canopy Sentinel-2A multi spectral remote sensing image of atmospheric correction, on this basis, using the cosine correction and Minnaert model for topographic correction in the study area. Among them, Minnaert model after correction, the mean and standard deviation were less than the cosine correction image after image, the image of the mean close to mean in atmospheric correction of images, removing all terrain shadows, reduces the slopes of contrast, eliminate or weaken the influence of the topography, the surface reflectance inversion combined with near. The measured data, the surface reflectance inversion image of mixed pixel decomposition based on linear model, the inversion reflectance of apple tree canopy. Through processing the image, apparent reflectance, surface reflectance inversion, the relative error of canopy reflectance and canopy reflectance inversion is gradually reduced. Numerical and canopy reflectance inversion of canopy the reflectance values are most similar, the relative error of 2~8A bands was 14.4%, 14.6%, 9.5%, 10.1%, 1.6%, 0.4%, 1.4% and 2%, that obtained through various image processing The more real canopy spectra, ensure accuracy provided for subsequent analysis. (2) the construction and screening of Apple Tree Canopy Chlorophyll vegetation index by considering the spectral characteristics of Sentinel-2A image and the green vegetation of the band, from RVI, CI, construction principle and form of NDVI, with the blue band 2 of Sentinel-2A, the green band 3 red, red edge band 4, band 7, band 8 near infrared and near infrared 8A to construct 12 vegetation indices, the correlation analysis and the content of chlorophyll, and the vegetation index correlation analysis, selected 3 vegetation index series, series 1, series 2 (RVIblue+RVIred+RVIre) (CIblue+CIred+CIre) and 3 Series (NDVIgreen+NDVIred+NDVIre). (3) establishment and verification of the Apple Tree Canopy Chlorophyll Content Retrieval Model Based on Vegetation Index Series 1, series 2 and 3 respectively as independent variable and the Apple Tree Canopy Chlorophyll Content As the dependent variable, established the inversion model of BP neural network and support vector machine regression model. The coefficient of determination BP neural network inversion model based on NDVIgreen+NDVIred+NDVIre vegetation index model and inspection of the 3 (Rc2=0.674, Rv2=0.601) were more than BP neural network inversion model 1 and model 2, the root mean square error (RMSEc=0.169, RMSEv=0.185) are less than the model 1 and model 2, better retrieval results. Support vector machine based on NDVIgreen+NDVIred+NDVIre vegetation index regression model 3 model and inspection decision coefficient is greater than the support vector machine regression model 1 and model 2, respectively 0.729 and 0.667, the root mean square error is less than model 1 and model 2, respectively. 0.159 and 0.178, the inversion result is good. The support vector machine regression model 3 is better than BP neural network inversion model 3, show that the support vector machine regression model 3 Effect Good, can be a good inversion of Apple Canopy Chlorophyll content, also show the effectiveness of Sentinel-2A image in Canopy Chlorophyll inversion. In summary, near ground hyperspectral data with the Sentinel-2A remote sensing image, which provides a new method for low mountain and hilly area of Apple Tree Canopy Chlorophyll content of the macro monitoring and rapid diagnosis, provides the theory the basis and technical support for the development of agricultural information.

【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】：S661.1;S127

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