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  本文關(guān)鍵詞：基于高光譜數(shù)據(jù)的冬小麥葉綠素含量估算模型　出處：《河北師范大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 葉綠素含量 高光譜 冠層反射率 PROSAIL模型 神經(jīng)網(wǎng)絡(luò) 支持向量機(jī)

【摘要】：綠色植被在生態(tài)系統(tǒng)中扮演著不可或缺的角色,葉綠素作為表征植被生長(zhǎng)狀況和發(fā)育狀況的重要因子,成為水文、氣候、土壤、生態(tài)等循環(huán)過(guò)程中的重要輸入?yún)?shù),被廣泛應(yīng)用于氣候變化、生態(tài)循環(huán)變化等研究中。同時(shí),對(duì)農(nóng)作物葉綠素含量的監(jiān)測(cè),還可以為農(nóng)業(yè)管理者提供決策信息,實(shí)現(xiàn)農(nóng)業(yè)變量水、變量肥管理,提高水肥利用率,對(duì)發(fā)展可持續(xù)的現(xiàn)代農(nóng)業(yè)具有重要的意義。本文利用2014年4月28日至5月2日和2015年4月25日和26日,野外試驗(yàn)和室內(nèi)實(shí)驗(yàn)收集的冬小麥LAI、平均葉傾角(MTA)、株高、葉綠素含量、含水量和光譜反射率等數(shù)據(jù),利用PROSAIL模型模擬了小麥冠層反射率曲線,并與實(shí)測(cè)小麥冠層反射率曲線進(jìn)行對(duì)比,分析了葉綠素含量、LAI水平、MTA水平和含水量對(duì)小麥反射率曲線的影響,分析了不同觀測(cè)天頂角對(duì)植被冠層反射率的影響。分析了不同葉綠素含量對(duì)紅邊幅值、紅邊面積、NDVI、MCARI和CIred edge的影響,并根據(jù)模擬的反射率曲線提取了紅邊幅值、紅邊面積、NDVI、MCARI和CIred edge建立了葉綠素含量線性反演模型、反向神經(jīng)網(wǎng)絡(luò)(BPNN)反演模型、支持向量機(jī)(SVM)反演模型,并驗(yàn)證了其反演精度�；诓煌挠^測(cè)天頂角建立了NDVI、MCARI和CIred edge的線性反演模型、BPNN反演模型、SVM反演模型,并驗(yàn)證了其反演精度,得到以下結(jié)論:(1)利用PROSAIL模型模擬了某樣點(diǎn)的冠層反射率曲線與野外試驗(yàn)利用SVC測(cè)得的該樣點(diǎn)的反射率曲線進(jìn)行了對(duì)比分析,模型模擬的反射率曲線與實(shí)測(cè)反射率曲線走勢(shì)一致,在可見(jiàn)光范圍內(nèi)反射率值相差無(wú)幾,在780nm以后模型模擬的反射率值稍高一些,說(shuō)明了PROSAIL模型能夠很好的模擬小麥冠層反射率。(2)對(duì)植被冠層反射率的敏感性分析發(fā)現(xiàn):葉綠素含量主要在可見(jiàn)光范圍內(nèi),對(duì)植被的冠層反射率有影響,隨著葉綠素含量的增加反射率值減小;LAI對(duì)反射率曲線的影響主要在近紅外波段,隨著LAI值的增加,植被冠層光譜反射率增加;平均葉傾角(MTA)對(duì)植被冠層反射率的影響與LAI相反,在近紅外波段隨著平均葉傾角的增大,反射率值減小;植被含水量對(duì)其冠層反射率的影響波段為近紅外波段,隨著含水量的增加反射率值減小。(3)利用PROSAIL模型模擬了不同觀測(cè)天頂角的植被冠層光譜反射率,選擇了三個(gè)角度0°、36°、55°,在植被生化組分含量不變時(shí),同一波段內(nèi)的植被冠層光譜反射率隨著觀測(cè)天頂角的增大而上升。(4)分析了葉綠素含量對(duì)紅邊幅值、紅邊面積、NDVI、MCARI和CIred edge的影響發(fā)現(xiàn):隨著葉綠素含量的增加紅邊幅值、紅邊面積、NDVI和CIred edge的值也呈線性增加,而MCARI的值隨著葉綠素含量的增加,逐漸減小。(5)利用紅邊幅值、紅邊面積、NDVI、MCARI、CIred edge建立了葉綠素估算模型,并進(jìn)行了精度驗(yàn)證,結(jié)果表明:在線性估算模型中,MCARI和CIred edge的估算模型的精度最高,相關(guān)系數(shù)R2分別為0.95和0.939,均方根誤差分別為2.789和2.806,相對(duì)誤差分別為0.45和0.048�；诓煌挠^測(cè)天頂角建立了NDVI、MCARI、CIred edge的線性估算模型,在線性估算模型中,觀測(cè)天頂角為55°時(shí),CIred edge估算模型的反演精度最好,其模型相關(guān)系數(shù)R2為0.953,均方根誤差和相對(duì)誤差分別為7.088和0.094。(6)利用BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型和支持向量機(jī)預(yù)測(cè)模型建立了葉綠素估算模型。在BPNN反演模型中,基于MCARI和CIred edge的建立的神經(jīng)網(wǎng)絡(luò)反演模型反演效果最好,MCARI模型的均方根誤差為2.809,相對(duì)誤差僅為0.046;CIred edge模型的均方根誤差為2.600,相對(duì)誤差為0.927。在SVM反演模型中,基于MCARI建立的模型反演效果最好,其均方根誤差為2.863,相對(duì)誤差為0.045。(7)當(dāng)觀測(cè)天頂角不同時(shí),BPNN反演模型中,觀測(cè)天頂角為0°時(shí),CIred edge-BPNN模型的均方根誤差和相對(duì)誤差分別為7.265和0.107;SVM反演模型中,觀測(cè)天頂角為55°,CIred edge-SVM模型的均方根誤差和相對(duì)誤差分別為7.185和0.095。
[Abstract]:Green vegetation plays an indispensable role in the ecological system, an important factor, chlorophyll as the characterization of vegetation growth situation and development status of the climate, soil, hydrology and become, important input parameters of ecological cycle process, is widely used in the study of climate change, ecological cycle changes. At the same time, the monitoring of chlorophyll content in crops also, for agricultural managers to provide decision-making information, realize the variable water agriculture, variable fertilizer management, improve water and fertilizer use efficiency, is of great significance to the sustainable development of modern agriculture. This paper from April 28, 2014 to May 2nd and April 25, 2015 and 26, the winter wheat LAI collected field test and laboratory experiment, the mean leaf angle (MTA) and the plant height, chlorophyll content, water content and spectral reflectance data to simulate the wheat canopy reflectance curve using the PROSAIL model, and with the measured wheat canopy The reflectivity curves are compared and analyzed the content of chlorophyll, LAI level, MTA level and the effect of moisture content on wheat reflectance curve, analyzes the influence of different zenith angle on canopy reflectance. Analysis of the amplitude of different chlorophyll contents of red edge, red edge area, NDVI, MCARI and CIred influence edge, and extracted. The red edge amplitude according to the simulation of the reflectivity, red edge area, NDVI, MCARI and CIred edge to establish the linear inversion model of chlorophyll content, BP neural network (BPNN) inversion model, support vector machine (SVM) inversion model, and verified the inversion precision. Different view zenith angle is established based on the linear NDVI. MCARI and CIred edge model inversion, BPNN inversion model, SVM inversion model, and verified the accuracy of inversion, we get the following conclusions: (1) simulated canopy reflectance curve of a sample point and field test using PROSAIL model The reflectance curves using SVC measured the samples were compared and analyzed, simulated reflectivity curves and measured reflectance curve consistent with the trend, in the range of visible light reflectance values not much difference between after 780nm reflectance model, the simulation value is slightly higher, the PROSAIL model can simulate the wheat canopy reflectance as well. (2) the sensitivity analysis of vegetation canopy reflectance showed that chlorophyll content mainly in the visible range, impact on vegetation canopy reflectance, chlorophyll content increased with the decrease of reflectivity; the effect of LAI on the reflectance curve is mainly in the near infrared band, with the increase of LAI value, increasing vegetation canopy reflectance; the mean leaf angle (MTA) effect on vegetation canopy reflectance and LAI on the contrary, in the near infrared band increases with the mean leaf angle, reflectivity decreases; vegetation water content on the crown Effect of band reflectance layer for near infrared band, along with the increase of water content in reflectance values decreases. (3) simulated vegetation canopy spectral reflectance of different zenith angle by using the PROSAIL model, choose the three angles of 0 degrees, 36 degrees, 55 degrees, in the vegetation biochemical component unchanged, vegetation canopy spectral reflectance the same band increases with the increasing of viewing zenith angle. (4) analysis of the amplitude of the chlorophyll content of the red edge, red edge area, NDVI, MCARI and CIred found that the influence of edge: with the increase of the content of the chlorophyll red edge amplitude, red edge area, NDVI and CIred edge values increased linearly while the MCARI value decreases gradually with the increase of chlorophyll content. (5) the use of red edge amplitude, red edge area, NDVI, MCARI, CIred, edge established the chlorophyll estimation model, and the accuracy was verified, the results show that: in the linear estimation model, MCARI and CIred edge The estimation model of the highest accuracy, the correlation coefficient R2 were 0.95 and 0.939, the root mean square error were 2.789 and 2.806 respectively, the relative error is 0.45 and 0.048. NDVI, based on different zenith angle MCARI, CIred linear edge estimation model, the linear estimation model, the zenith angle is 55 degrees CIred edge, estimated the accuracy of model inversion is the best model, the correlation coefficient R2 is 0.953 and the root mean square error and relative error were 7.088 and 0.094. (6) model of chlorophyll estimation model and SVM prediction model using BP neural network. The BPNN inversion model, MCARI and CIred edge of nerve network inversion model inversion effect based on the best, the root mean square error of MCARI model is 2.809, the relative error is only 0.046; the root mean square error of CIred edge model is 2.600, the relative error is 0.927. in SVM inversion model, Model inversion based on MCARI best, the root mean square error is 2.863, the relative error is 0.045. (7) when the zenith angle is not at the same time, BPNN inversion model, the zenith angle is 0 degrees, the root mean square error of CIred edge-BPNN model and the relative errors are 7.265 and 0.107; SVM inversion model. The zenith angle is 55 degrees, the root mean square error CIred edge-SVM model and the relative errors were 7.185 and 0.095.

【學(xué)位授予單位】：河北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S512.11

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