【作者】 姚霞；

【导师】 曹卫星；

【作者基本信息】 南京农业大学 ， 作物信息学， 2009， 博士

【摘要】 定量监测植物氮素状况已成为国内外植被遥感的一个重要研究领域。利用快速、无损、准确的方法来估测作物氮素营养状况是精确农业发展的关键技术之一。本研究的目的是以小麦为对象,基于不同年份、不同氮素水平和不同品种类型的田间试验,在冠层和单叶两个尺度上,运用精细光谱分析法,挖掘高光谱海量信息,探索指示小麦氮素营养状况的核心波段和敏感参数,建立准确而适用的小麦冠层和单叶氮素营养指标监测模型,从而为便携式作物氮素营养监测仪的研制开发及空间遥感信息的解析利用提供核心波段选择,为小麦氮素营养的实时监测和精确诊断提供有效技术支撑。首先在明确小麦冠层叶片氮素状况随施氮水平和生育进程动态变化的基础上,利用减量精细采样法,系统分析了350-2500 nm范围内任意两波段的原始光谱反射率及其一阶导数组成的归一化光谱指数(NDSI).比值光谱指数(RSI)、土壤调节光谱指数(SASI)与冠层叶片氮素营养状况的定量关系,进而构建了基于核心波段和敏感参数的小麦冠层氮素营养监测方程。结果显示,对小麦氮素营养反应敏感的反射光谱主要位于可见光区和近红外区,利用NDSI(R1350, R700)、NDSI(FD690, FD700)、RSI(FD691, FD711)估算小麦冠层叶片氮含量准确性较高；基于NDSI(R860, R720)、NDSI(FD736,FD526)、RSI(R990, R720)预测小麦冠层叶片氮积累量稳定性较强。进一步分析了SASI中参数L的最佳取值,发现当L的取值为0.09和0.3时,SASI(R1350, R700)和SASI(R860, R720)的表现最好。比较而言,冠层氮积累量监测模型的表现好于氮含量监测模型。基于小麦冠层氮素营养最佳光谱指数,进一步分析了核心波段的光谱分辨率变化对光谱指数及氮素估测精度的影响。结果显示,光谱分辨率对不同光谱指数(NDSI和RSI)的影响有所差异。NDSI(R1350,R700)的核心波段1350nm<20nm带宽,700 nm< 60 nm带宽,其值较稳定；而NDSI(R860, R720)的核心波段860 nm< 96 nm带宽,720 nm<26 nm带宽,其值较稳定。随带宽的变化,RSI(R697,R1155)在697 nm方向缓慢降低,在1155 nm方向逐渐升高；RSI(R990, R720)在990 nm方向的60 nm带宽内变化速率较大,在720 nm方向的变化速率较小。同时发现基于不同光谱分辨率的光谱指数对氮素营养指标的估测精度和稳定性也表现不同。当NDSI(R1350, R700)中的两波段的带宽分别小于74 nm和46 nm时,表现相对比较稳定可靠；NDSI(R860, R720)中的两波段的带宽分别小于96nm和26 nm时表现均较佳。而RSI(R697, R1155)中两波段的带宽分别小于4 nm和6 nm时表现效果较好；RSI(R990, R720)中两波段的带宽分别小于14 nm和14 nm时表现均优秀。通过分析不同类型红边参数与小麦冠层叶片氮素营养状况的定量关系及统计特征,比较了不同类型红边参数(红边位置、红边斜率和红边面积及其他红边衍生参数)预测冠层叶片氮素营养指标的准确性和可靠性,进而确定了监测小麦冠层氮素营养指标的最佳红边参数及预测方程。结果表明,小麦冠层红边一阶导数光谱具有“双峰”或“多峰”现象,红边位置位于690-730 nm之间。随施氮水平的提高,红边向长波方向移动,红边斜率增高,红边面积增大；随生育期的推移,红边先向长波方向移动(红移),后向短波方向移动(蓝移),红边位置、红边斜率和红边面积均先逐渐增大,至孕穗期后逐渐减小；不同品种间表现基本一致。在几类红边参数中,基于线性外推法获得的红边位置和红边最小值可以稳定地指示小麦冠层叶片氮素含量；基于线性外推法获得的红边位置和基于倒高斯模型的左右峰面积差能够可靠地监测小麦叶片氮积累量；且小麦冠层叶片氮积累量监测模型的表现好于氮含量模型。对基于ASD的高光谱反射率数据进行重采样,系统模拟了不同卫星通道原始光谱反射率及其光谱指数与小麦冠层叶片氮素营养状况的定量关系,比较了多种星载传感器在小麦冠层叶片氮素营养估测中的敏感性和稳定性。发现可以利用NDVI(MSS7, MSS5)、NDVI(RBV3, RBV2)、TM4、CH2、MODIS1和MODIS2的遥感数据来预估小麦冠层叶片氮含量；应用NDVI(PB4, PB2)、NDVI(CH2, CH1). NDVI(MSS7, RVI(MSS7, MSS5)、MODIS,和MODIS2预测小麦冠层叶片氮积累量。比较而言,NDVI(MSS7, MSS5)和NDVI(PB4, PB2)分别为预测小麦冠层叶片氮含量和氮积累量的适宜星载通道植被指数,指出MSS7、MSS5、LANDSAT和IRS-P6的卫星遥感影像数据在作物氮素监测中具有广泛应用前景。在阐明不同试验条件下不同生育时期小麦单叶光谱反射率和氮含量变化模式的基础上,分析确立了基于小麦单叶高光谱参数(新建的植被指数、红边参数及已报导的光谱参数)监测冠层叶片氮含量的可行性。结果表明,顶二叶和顶三叶对冠层叶片氮素的预测能力较强。利用顶一叶的NDSI(R429, R477)、RSI(R429, R498),顶二叶的NDSI(R610, R480)、RSI(R610, R480),顶三叶的NDSI(R1821, R571)、RSI(R1821, R571)和顶四叶的NDSI(R654, R663)、RSI(R663, R654)来预测冠层叶片氮含量时表现较好。基于单叶红边参数预测冠层叶片氮含量时,红边最小值、基于倒高斯的红边位置、红边对称度、红谷对应波长分别为顶一、顶二、顶三和顶四叶上表现较好的参数。采用已报道的光谱指数预测时,发现顶一、顶二、顶三和顶四叶PRIa、RSI(R560,R450)、FD723、FD612可以有效预测冠层叶片的氮含量。另外,采用不同叶位组合的高光谱参数预测冠层叶片氮含量时,DVI[NDSI(R1821, R571)3, NDSI(R610, R48o)2], NDSI[RSI(R1821, R571)3, RSI(R610, R480)2]表现最好,且远远好于其他光谱参数。基于单叶最佳光谱指数,进一步分析了各单叶敏感波段的光谱分辨率变化对小麦冠层氮含量监测方程的影响。结果显示,利用顶部4张单叶的最佳光谱指数监测小麦冠层氮含量时,其光谱分辨率存在一个适宜区域,在该区域内的冠层氮素估测精度没有显著差异。在单叶水平上系统分析了新构建的最佳光谱指数、红边相关参数及已有氮素相关光谱指数与顶部叶片氮含量的定量关系。结果显示,随施氮水平的提高,顶部4张叶片的氮含量均增加,光谱反射率在可见光区降低,在近红外和中红外区升高；近红外波段的光谱反射率显著高于可见光波段。随生育期的推移,顶部4张叶片的氮含量先升后降,光谱反射率先降后升。顶部4张单叶的光谱指数NDSI(R510,R430)、NDSI(R620, R480)、NDSI(R622, R426)和RSI(R421, R655)可以较准确地依次定量估算小麦植株顶部4张单叶的氮含量；且光谱指数NDSI(R613,R426)能综合估算小麦植株顶部4张叶片的单叶氮含量。红边区域受施氮水平和品种影响较大,在703-742 nm处能显著区分不同施氮水平；红边最小值和红边对称度可以估测顶部4张单叶的氮含量；ND705、mND705是2个比较通用的监测小麦单叶氮素营养指标的光谱参数。进一步分析了各单叶敏感参数的光谱分辨率变化对单叶氮含量估测精度的影响,表明基于单叶最佳光谱指数监测小麦单叶氮含量的光谱分辨率,在特定范围内对单叶氮素估测能力保持相对稳定。更多还原

【Abstract】 Quantitative monitoring of plant nitrogen status is an important research field in vegetation remote sensing. Fast and non-destructive and accurate estimation of crop nitrogen status is one of key techniques in development of precision agriculture. The primary objective of this study was to explore the optimum wavebands, spectral indices and quantitative models for estimating leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) through systematic extraction of hyperspectral information at canopy and leaf levels, on the basis of multiple field experiments under varied N rates and cultivars in wheat. The anticipated results would provide new waveband choice for manufacturing portable N monitoring instrument and utilizing space-borne remote sensing data, and thus assist in real-time estimation and precise diagnosis of plant nitrogen status in wheat.Firstly, the change patterns of canopy leaf nitrogen status over growth progress under varied nitrogen levels were established, and then a reduced precise sampling method was adopted for comprehensive analysis of the quantitative relationships of LNC and LNA to NDSI (normalized difference spectrum index, NDSI) and RSI(ratio spectrum index, RSI) composed of any two wavebands with original reflectance and its derivative within the spectral range of 350-2500 nm, and to SASI (soil adjusted spectrum index, SASI) and selected best spectrum index with different bandwidths. From the derived core bands and sensitive parameters, the monitoring equations were constructed for LNC and LNA in wheat. The results showed that the sensitive wavebands of nitrogen status were mostly located in the visible and near-infrared regions. The LNC monitoring models developed from NDSI(R1350, R700), NDSI(FD700, FD690) and RSI(FD691, FD711) gave high estimation accuracy. And the spectral indices NDSI(R86o, R720), NDSI(FD736, FD526) and RSI(R99o, R720) could be reliably used for estimating LNA. Furthermore, analysis of L parameter in SAVI revealed that the models constructed on SASI(R1350, R700) and SASI(R860, R720) had best estimation with L parameter as 0.09 and 0.3, respectively. Comparatively, the LNA models performed better than the LNC models for wheat canopy.Further analysis was conducted on the responses of the optimum spectral indices and monitoring models to the changes of spectral resolution based on the sensitive bands. The results revealed that the impact of spectral resolution differed with spectral index. The changes for NDSI(R1350, R700) and NDSI(R860, R720) were relatively stable with the resolution of four key bands less than 20 nm,60 nm,96 nm and 26 nm, respectively. With varied wavebands, the values for RSI(R697, R1155) gradually decreased in the direction of 697 nm, but enhanced in 1155nm. The RSI(R99o, R720) exhibited fast changes within 60nm bandwidth in the direction of 990 nm, but slow changes in 720nm. In addition, it was found that the spectral indices based on varied spectral resolution of key bands generated differential prediction accuracy and stability for wheat nitrogen status. The monitoring models based on NDSI(R1350, R700) and NDSI(Rg6o, R720) displayed stable performance with the resolution of four key bands less than 74 nm,46 nm,96 nm and 26 nm, respectively. And the models from RSI(R697, R1155) and RSI(R99o, R720) were excellent with the resolution of four bands less than 4 nm,6 nm,14 nm and 14 nm, respectively.On the basis of analyzing the quantitative relationships and statistical characters between different types of red edge parameters (including red edge position, red edge derivative, red edge area and other red derivative paremeter) and canopy leaf nitrogen status, the monitoring models were developed for canopy leaf nitrogen nutrient in wheat by comparing accuracy and reliability of nitrogen estimation. The results exhibited a dual-peak or multi-peak feature with the first derivative spectra in the red edge region of 690-730nm. With increasing nitrogen levels, the red edge position (REP) moved to the longer wavelength and the red edge derivative increased, thus enlarging the red edge area. With progress of the growth stages, the REP moved first to the longer wavelength and then to shorter wavelength, and three red edge parameters all gradually increased and then decreased after booting. These change patterns were consistent among different cultivars. Of several red edge parameters, the monitoring models developed from the REP-LEM (linear extrapolation method) and the minimum RED could stably indicate LNC, and the models on the REP-LEM and the difference between left and right REA (red edge area) from IGAUS could reliably estimate LNA, with better performance from LNA models than from LNC models in wheat canopy.By re-sampling hyperspectral data from ASD measurements, the relationships were systematically quantified of canopy leaf nitrogen status to the simulated spectral parameters including the single wavelength, ratio spectral index and normalized difference spectral index, and the capacity and stability of estimating canopy leaf nitrogen status were compared based on simulated satellite channels. The results indicated that the spectral parameters based on NDVI(MSS7, MSS5), NDVI(RBV3, RBV2), TM4, CH2, MODIS1 and MODIS2 could be reliably used for estimating LNC, and those on NDVI(PB4, PB2), NDVI(CH2, CH1), NDVI(MSS7, MSS5), RVI(MSS7, MSS5), MODIS1 and MODIS2 could be used for predicting LNA, with better performance from LNA models than from LNC models in winter wheat. In comparison, NDVI(MSS7, MSS5) and NDVI(PB4, PB2) were the best spectral indices for monitoring LNC and LNA in wheat canopy, respectively. Furthermore, MSS7, MSS5, LANDSAT and IRS-P6 should be of wider application prospect in monitoring of crop nitrogen status.By elucidating the change patterns of the single leaf hyperspectrum and canopy leaf nitrogen content at different growth stages, analysis was made on the accuracy and stability of monitoring canopy nitrogen status with the single leaf hyperspectral parameters including the new spectral indices, red parameters, and reported nitrogen indices. The results showed that the spectral indices of the 2nd and 3rd leaves had the stronger capacity to estimate the canopy leaf nitrogen content (LNC), and could be considered as the indicator of canopy LNC in wheat. The parameters NDSI(R429, R477) and RSI(R429, R498) of the 1st leaf, NDSI(R610, R480) and RSI(R610, R480) of the 2nd leaf, NDSI(R1821, R571) and RSI(R1821, R571) of the 3rd leaf, and NDSI(R654, R663) and RSI(R663, R654) of the 4th leaf could be used for reliably predicting canopy LNC. Of several red edge parameters, the minimum RED, REP-IGAUS, symmetry of REP, and position of the red vale were the best indices for evaluating canopy LNC based on the 1st,2nd,3rd and 4th leaf, respectively. With the previously reported spectral indices, PRIa, RSI(R560, R450), FD723 and FD612 were found to be the proper parameters for canopy LNC on the 1st,2nd,3rd and 4th leaf, respectively. In addition, with the spectral indices of four combined leaves, the monitoring equations based on DSI[NDSI(R1821, R571)3, NDSI(R610, R480)2], NDSI[RSI(R1821, R571)3, RSI(R610,R480)2] had better performance than all the above indices. Further analysis was made on the responses of the monitoring models to the spectral resolution of sensitive bands in the best spectral index. It was found that there was a suitable range for the spectral resolution of key bands with the individual top leaves, in which estimation accuracy of the canopy LNC did not show obvious difference.The quantitative relationships were analyzed between the leaf nitrogen contents and the new spectral indices, red edge parameters, and reported nitrogen indices at leaf level. The results revealed that the nitrogen contents in individual top leaves obviously exhibited spatial distribution pattern, markedly influencing corresponding spectral reflectance. With increasing nitrogen rates, the nitrogen contents in the top four leaves gradually increased, while the spectral reflectance decreased in the visible region but enhanced in the near-mid-infrared regions, with much higher reflectance values than in the visible region. With the growth progress, the leaf nitrogen content first increased and then decreased, while the spectral reflectance showed opposite pattern, essentially consistent in top four leaves. The spectral parameters NDSI(R510, R430), NDSI(R62o, R480), NDSI(R622, R426) and RSI(R421, R655) could be reliably used for quantifying the nitrogen contents of the 1st,2nd, 3rd and 4th leaf in the top, respectively, while the NDSI(R613, R426) could generally estimate the nitrogen contents in the top four leaves. The red edge region was greatly affected by the nitrogen rates and cultivar types, and could markedly differentiate the nitrogen levels in the spectral region of 703-742nm. The minimum RED and red edge symmetry could estimate the nitrogen contents of four combined leaves. In addition, ND705 and mND705 were more general spectral parameters for monitoring of nitrogen content at leaf level in wheat. Further analysis was conducted on the effects of the spectral resolution of key bands in the sensitive parameters on prediction accuracy for single leaf nitrogen content. It was found that there was a proper range for spectral resolution, in which estimation ability of the LNC model was relatively stable.更多还原