發(fā)布時間：2018-07-06 18:43

  本文選題：水稻 + 高光譜��； 參考：《華中農業(yè)大學》2017年碩士論文

【摘要】：水稻是主要糧食作物之一,其生長狀況的監(jiān)測對水稻生長管理、產量預測、災害防治等方面起到重要作用。而葉面積指數(shù)、色素含量等生理生化參數(shù)是水稻長勢監(jiān)測的重要指標。憑借高光譜技術的波段連續(xù)性強、波譜分辨率高、光譜信息豐富等優(yōu)勢,能夠實時、快速、高效、無損地獲取水稻長勢、營養(yǎng)狀況及其變化狀況,為精準農業(yè)的信息化管理提供技術支持和理論依據。本文以不同氮肥水平、不同生育期的水稻為研究對象,建立水稻生理生化參數(shù)的冠層反射光譜反演模型,基于環(huán)境衛(wèi)星(HJ-1A)影像數(shù)據,獲得水稻關鍵生長期的葉面積指數(shù)(Leaf area index,LAI)的空間分布,實現(xiàn)水稻生長狀況的大范圍觀測。圍繞上述內容,開展研究,取得了如下主要結果:1.基于水稻冠層反射光譜數(shù)據得到15個植被指數(shù),利用留一法交叉驗證進行5種傳統(tǒng)回歸模型分析(線性函數(shù)、指數(shù)函數(shù)、對數(shù)函數(shù)、冪函數(shù)、多項式函數(shù)),建立了不同生育期的水稻LAI高光譜指數(shù)估計模型,獲取了不同生育期的優(yōu)選植被指數(shù),采用噪聲等效誤差(Noise Equivalent,NE)對植被指數(shù)反演LAI進行了敏感性分析,結果顯示,分蘗期歸一化植被指數(shù)(normalized difference vegetation index,NDVI)、新型植被指數(shù)(new vegetation index,NVI)對LAI變化敏感,且估計精度高;拔節(jié)期綠色歸一化植被指數(shù)(green normalized difference vegetation index,GNDVI)、比值植被指數(shù)(ratio vegetation index,RVI-3)、改進的簡單比值指數(shù)(modified simple ratio index,MSR)具有高敏感性和估計精度;水稻生長后期GNDVI、修正歸一化差異植被指數(shù)(modified normalized difference vegetation index,mNDVI)、MSR比其他指數(shù)更適用于LAI估計�；谥脖恢笖�(shù)構建的全生育期水稻LAI傳統(tǒng)回歸模型精度過低,難以用統(tǒng)一植被指數(shù)來估算整個生育期水稻LAI,利用偏最小二乘回歸建模方法,建模集和驗證集的決定系數(shù)R2分別可達到0.87和0.81,RMSEC為0.612,RMSEP為0.856,RPD大于2,能夠較為精確地估算全生育期水稻LAI。2.基于連續(xù)統(tǒng)去除處理的水稻冠層高光譜數(shù)據(400~750 nm),選取了波段深度(band depth,BD)、波段深度比(band depth ratio,BDR)、歸一化波段深度(normalized band depth index,NBDI)和歸一化面積波段指數(shù)(band depth normalized to band area,BNA)4種波段指數(shù)。在此基礎上進行主成分分析(principal component analysis,PCA)實現(xiàn)光譜降維,然后運用反向傳播(back propagation,BP)神經網絡方法對水稻葉片色素含量進行高光譜反演。結果表明,BD與BP結合的估算模型對水稻葉片中的類胡蘿卜素含量估算精度最高(R2=0.61,RMSEP=0.128 mg·g-1),BNA與BP結合的估算模型對水稻葉片中的葉綠素含量估算精度最高(R2=0.73,RMSEP=0.343 mg·g-1)。對比分析BDA與BP結合的模型和植被指數(shù)最佳回歸模型的精度,發(fā)現(xiàn)波段深度分析建立的BP神經網絡模型能較好地解決飽和問題,提高水稻葉片色素含量的估算精度。3.基于大田地面調查點和小區(qū)試驗田的水稻灌漿期冠層反射光譜,根據HJ-1A衛(wèi)星影像的光譜響應函數(shù),模擬HJ-1A衛(wèi)星的藍、綠、紅、近紅外波段。對12個植被指數(shù)與水稻LAI的相關性進行分析,選取相關系數(shù)最大的植被指數(shù)構建水稻LAI估算模型。結果表明,GRVI的二次多項式回歸模型估算水稻灌漿期LAI的精度最高,模型為LAI=-0.027*GRVI2+1.125*GRVI+0.028,建模集和驗證集的R2分別達到0.89和0.80,RMSEC和RMSEP均較低,RPD大于2,模型優(yōu)異。利用GRVI-LAI估算模型,獲取水稻灌漿期LAI的空間分布圖。由于HJ-1A衛(wèi)星影像受大氣狀況和空間分辨率較低所限,空間分布圖上的LAI預測值普遍低于地面對應點的LAI實測值。
[Abstract]:Rice is one of the main grain crops. Monitoring of its growth condition plays an important role in rice growth management, yield prediction, and disaster prevention and control. The leaf area index, pigment content and other physiological and biochemical parameters are important indicators of rice growth monitoring. The advantages of rich and so on can provide real-time, rapid, efficient and nondestructive rice growth, nutritional status and its change status, and provide technical support and theoretical basis for the information management of precision agriculture. In this paper, a canopy reflectance spectral inversion model of rice physiological and biochemical parameters was built with different nitrogen fertilizer levels and different growth stages. Based on the environmental satellite (HJ-1A) image data, the spatial distribution of the leaf area index (Leaf area index, LAI) of the critical growth period of rice was obtained, and the growth of rice was observed in a wide range. The following main results were carried out around the above content. 1. the main results were as follows: 15 vegetation indices were obtained based on the canopy reflectance spectrum data of rice. 5 traditional regression model analysis (linear function, exponential function, logarithmic function, power function, polynomial function) were carried out by the method of cross validation. The LAI hyperspectral index estimation model of rice at different growth stages was established, the optimum vegetation index was obtained at different growth stages, and the Noise Equivalent (NE) was used for the inversion of LAI into the vegetation index. The results showed that the normalized vegetation index (normalized difference vegetation index, NDVI) at the tillering stage, and the new vegetation index (new vegetation index, NVI) were sensitive to the change of LAI, and the accuracy was high, and the green normalized vegetation index (green normalized) at jointing stage, and the ratio vegetation index. Ratio vegetation index (RVI-3), the improved simple ratio index (modified simple ratio index, MSR) has Gao Min sensibility and estimation accuracy; the later GNDVI of the rice growth, the revised normalized difference vegetation index (modified normalized) is more applicable than the other indices. The traditional regression model of rice LAI in the whole growth period is too low, it is difficult to estimate the whole growth period rice LAI with unified vegetation index. Using partial least square regression modeling method, the decision coefficient R2 of modeling set and verification set can reach 0.87 and 0.81, RMSEC is 0.612, RMSEP is 0.856, RPD is greater than 2, and the whole life can be estimated more accurately. The rice LAI.2. is based on the hyperspectral data (400~750 nm) of the rice canopy based on the continuous removal of the rice (band depth, BD), the band depth ratio (band depth ratio, BDR), the normalized band depth (normalized band) and the normalized area band index (nm). Index. On this basis, principal component analysis (PCA) is used to achieve spectral dimensionality reduction, and then inverse propagation (back propagation, BP) neural network method is used for hyperspectral inversion of rice leaf pigment content. The results show that the estimation model of the combination of BD and BP is used to estimate the carotenoid content in rice leaves The accuracy is highest (R2=0.61, RMSEP=0.128 mg. G-1). The estimation accuracy of the estimation model combined with BNA and BP is the highest (R2=0.73, RMSEP=0.343 mg g-1). The accuracy of the model and the optimal regression model of the vegetation index is compared and analyzed. In order to solve the problem of saturation, the estimation accuracy of rice leaf pigment content was improved.3. based on the canopy reflectance spectra of rice grain filling stage in field ground survey point and plot test field. According to the spectral response function of HJ-1A satellite images, the blue, green, red and near infrared bands of the HJ-1A satellite were simulated. The correlation between the 12 vegetation indices and rice LAI was analyzed. The estimation model of rice LAI was constructed with the largest correlation coefficient of vegetation index. The results showed that the two polynomial regression model of GRVI was the highest for estimating the precision of LAI in rice filling period. The model was LAI=-0.027*GRVI2+1.125*GRVI+0.028, the R2 of modeling set and verification set reached 0.89 and 0.80 respectively, RMSEC and RMSEP were lower, RPD was greater than 2, and the model was excellent. The GRVI-LAI model was used to obtain the spatial distribution map of LAI during the grain filling period. Because the HJ-1A satellite image was limited by the atmospheric condition and the low spatial resolution, the prediction value of the LAI on the spatial distribution map was generally lower than the measured value of the LAI at the ground corresponding point.
【學位授予單位】：華中農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S511;TP751

【參考文獻】

相關期刊論文 前10條

1 高林;楊貴軍;于海洋;徐波;趙曉慶;董錦繪;馬亞斌;;基于無人機高光譜遙感的冬小麥葉面積指數(shù)反演[J];農業(yè)工程學報;2016年22期

2 田明璐;班松濤;常慶瑞;由明明;羅丹;王力;王爍;;基于低空無人機成像光譜儀影像估算棉花葉面積指數(shù)[J];農業(yè)工程學報;2016年21期

3 賈方方;張黎明;任天寶;李夢竹;楊艷東;劉國順;;基于BP神經網絡的煙草葉片質體色素高光譜反演[J];煙草科技;2016年07期

4 王弘;施潤和;劉浦東;高煒;;植物光學模型估算葉片類胡蘿卜素含量的一種雙歸一化差值-比值植被指數(shù)[J];光譜學與光譜分析;2016年07期

5 李粉玲;常慶瑞;申健;王力;;基于GF-1衛(wèi)星數(shù)據的冬小麥葉片氮含量遙感估算[J];農業(yè)工程學報;2016年09期

6 馬文勇;王訓明;;基于高光譜分析的草地葉綠素含量估算研究進展[J];地理科學進展;2016年01期

7 辛明月;殷紅;陳龍;張美玲;任智勇;苗靜;;不同生育期水稻葉面積指數(shù)的高光譜遙感估算模型[J];中國農業(yè)氣象;2015年06期

8 高林;楊貴軍;王寶山;于海洋;徐波;馮海寬;;基于無人機遙感影像的大豆葉面積指數(shù)反演研究[J];中國生態(tài)農業(yè)學報;2015年07期

9 賈玉秋;李冰;程永政;劉婷;郭燕;武喜紅;王來剛;;基于GF-1與Landsat-8多光譜遙感影像的玉米LAI反演比較[J];農業(yè)工程學報;2015年09期

10 姜海玲;楊杭;陳小平;王樹東;李雪軻;劉凱;岑奕;;利用光譜指數(shù)反演植被葉綠素含量的精度及穩(wěn)定性研究[J];光譜學與光譜分析;2015年04期

相關碩士學位論文 前1條

1 范永東;模型選擇中的交叉驗證方法綜述[D];山西大學;2013年

，

本文編號：2103754