利用光谱反射率估算叶片生化组分和籽粒品质指标研究

对可见光至短波红外波段(350—2500nm)冬小麦田间冠层光谱反射率与叶片含氮量间的关系进行了相关分析。结果表明，820—1100nm波段的光谱反射率与叶片含氮量极显著正相关；1150—1300hm波段的光谱反射率与叶片含氮量显著正相关，以上两波段为叶片全氮的敏感波段。对各生育时期叶片全氮与其他生化组分的关系进行了回归分析，并建立了相关的回归方程，显著性检验结果表明，方程具有较高的可靠性。小麦的叶片含氮量可以估算其它生化组分及干物质指标含量，开花期叶片含氮量可用来估测籽粒蛋白质和干面筋等品质指标含量。     

利用光谱反射率估算叶片生化组分和籽粒品质指标研究

对可见光至短波红外波段(350—2500nm)冬小麦田间冠层光谱反射率与叶片含氮量间的关系进行了相关分析。结果表明，820—1100nm波段的光谱反射率与叶片含氮量极显著正相关；1150—1300hm波段的光谱反射率与叶片含氮量显著正相关，以上两波段为叶片全氮的敏感波段。对各生育时期叶片全氮与其他生化组分的关系进行了回归分析，并建立了相关的回归方程，显著性检验结果表明，方程具有较高的可靠性。小麦的叶片含氮量可以估算其它生化组分及干物质指标含量，开花期叶片含氮量可用来估测籽粒蛋白质和干面筋等品质指标含量。     

不同钾素处理春玉米叶片营养元素含量变化及其光谱响应

目的是研究不同钾营养水平春玉米典型生育期叶片的光谱响应,探索叶片内营养成分与叶片光谱反射率的相关性。方法是设置了不同梯度钾处理的盆栽试验,按玉米生育期进行光谱测定和取样分析。结果,通过对不同钾处理间玉米叶片养分含量的差异性分析表明,随着施钾的提高,叶片钾含量差异性达到显著水平。分析不同钾营养水平不同生育时期春玉米叶片光谱反射率与叶片钾含量的相关关系,并建立了喇叭口期利用叶片光谱反射率估测叶片钾含量的数学模型;以及分析了该处理下喇叭口期叶片内水分、叶绿素、氮、磷、钙、镁、锌、锰、铜、铁含量与叶片光谱反射率的相关性。结果表明：不同生育时期叶片钾含量与其光谱反射率的相关关系在光谱维方向存在明显差别,730—930nm和960—1100nm两波段为春玉米喇叭口期评价钾营养状况的敏感波段,光谱变量R767＋R1057,（R767＋R1057） /（logR767＋logR1057）和（R767-R1057） /（logR767-logR1057）均能很好的预测喇叭口期叶片钾含量;该时期叶片内不同成分与光谱反射率相关分析表明：550nm,710nm,950nm三波段处是各个相关曲线的突变点;叶片内各成分间高度相关的,它们的光谱相关曲线趋势也极为一致或对称。     

引黄灌区水稻红边特征及SPAD高光谱预测模型

叶绿素含量是评估水稻长势和产量的重要参数。为了实现快速而准确的叶绿素含量估测,以宁夏引黄灌区宁粳43号水稻为试验对象,通过不同的氮素水平试验,测定了水稻在拔节期、抽穗期和乳熟期的冠层高光谱反射率和叶片绿色度土壤、作物分析仪器开发(soil and plant analyzer development,SPAD)值,分析了水稻不同时期冠层光谱的红边变化特征,并建立了SPAD的估测模型。结果表明,水稻叶片SPAD值随供氮水平的增加而增加,随生育期的变化表现为至抽穗期达到最高,而后逐渐降低。冠层光谱反射率随供氮水平的提高在可见光波段降低,在近红外波段增加。冠层光谱的红边位置、红边幅值和红边面积从拔节期到抽穗期呈现出"红移",至乳熟期呈"蓝移"现象,三个红边参数均随氮素水平的提高而增加。水稻拔节期是以红边面积为变量建立的模型对SPAD预测能力较好,而抽穗期和乳熟期则是以红边位置为参数建立的模型精度较高,与南方稻田叶绿素估算模型有所差异。利用高光谱技术对水稻SPAD值进行定量反演,可为西北地区水稻长势遥感监测提供理论依据。     

冠层反射光谱对植被理化参数的全局敏感性分析

植被理化参数与许多有关植物物质能量交换的生态过程密切相关,定量分析植被反射光谱对理化参数的敏感性是遥感反演理化参数含量的前提。本文采用EFAST(Extended Fourier Amplitude Sensitivity Test)全局敏感性分析方法,利用PROSAIL辐射传输模型分析了冠层疏密程度对叶片生化组分含量、冠层结构以及土壤背景等多种参数敏感性的影响,并对植被理化参数反演所需先验知识的精度问题进行了初步探讨。研究表明:(1)对于较为稠密的冠层,可见光波段的冠层反射率主要受叶绿素含量的影响,近红外和中红外波段的冠层反射率主要受干物质量和含水量的影响;(2)对于稀疏的冠层,LAI是影响400—2500 nm波段范围内冠层反射率的最重要参数,土壤湿度次之,叶片生化参数对冠层反射率的敏感性较低;(3)在已知稀疏冠层LAI的情况下进一步确定土壤的干湿状态,可显著提高冠层反射率对叶绿素含量的敏感度,有助于稀疏冠层叶绿素含量的反演。     

不同供氮水平下水稻高光谱及其红边特征研究

通过大田和室内试验 ,测定了 3个供氮水平下 2个品种的水稻冠层、主茎叶片在不同发育期的高光谱反射率及对应的叶绿素、类胡萝卜素含量。结果表明 :不同供氮水平的水稻冠层和叶片光谱差异明显 ,其光谱反射率随供氮水平的提高在可见光范围降低 ,在近红外区域增高 ;拔节期和孕穗期主茎倒三叶反射率在可见光和近红外区域均高于倒一叶 ;冠层光谱红边位置λred、红边幅值Dλred和红边面积Sred孕穗前呈“红移” ,抽穗后呈“蓝移”现象 ;叶面积指数LAI、地上鲜生物量AFM、地上干生物量ADM和鲜叶重FLM与冠层光谱变量R12 0 0 /R550 ,R990 /R550 ,R80 0 /R550 ,R750 /R550 ,λred,Sred之间有极显著相关 ,冠层和叶片色素含量与其光谱变量R80 0 /R550 和λred之间也存在显著相关。这说明用合适的高光谱变量来估算水稻LAI,AFM ,ADM ,FLM和冠层、叶片的色素含量     

基于Hyperion影像的水稻冠层生化参量反演

采用小区实验与大田应用相结合的方法, 依据扬州实验小区地面实测拔节期、抽穗期及灌浆期的水稻叶片、冠层光谱及氮和叶绿素含量, 采用光谱吸收特征和植被指数分析方法, 得到估算水稻氮和叶绿素含量的最佳光谱特征参数; 结合覆盖江苏姜堰地区大田的Hyperion高光谱遥感影像, 建立反演水稻冠层氮和叶绿素含量的模型, 对研究区大田水稻冠层氮和叶绿素含量进行了反演及制图。结果表明: 经波深中心归一化方法分析, 发现以670nm为中心的光谱吸收特征面积与水稻氮含量呈显著相关性; 基于反转归一化光谱, 结合560nm和670nm两个波段, 建立的植被指数NDVI560_670能很好地反演水稻叶绿素含量。     

一种三江平原湿地的植被冠层叶绿素反演

针对三江平原洪河湿地保护区内主要特征植被冠层的叶绿素含量,采用PROSAIL模型从物理角度进行反演。首先将叶面积指数、叶片结构参数、等价水厚度、叶绿素实测含量等一些植被理化参数的实测值输入模型得到模拟光谱数据,然后与实测光谱数据对比验证其准确性。在模型中,通过固定其他参量不变,取叶绿素含量为唯一值时,考察在不同叶面积指数下叶绿素含量对冠层反射率的影响。结果显示,植被冠层叶绿素含量的敏感波段为555nm和720nm。基于PROSAIL模型的叶绿素反演方法较传统的统计模型相比是较好且稳健的方法。     

改进Sobol算法支持下的PROSAIL模型参数全局敏感性分析

定量分析模型参数的敏感性是构建参数反演模型的关键步骤。本文采用改进Sobol全局敏感性分析算法,对PROSAIL模型的输入参数进行全局敏感性分析。结果表明:①在可见光波段430~760 nm范围内,叶绿素含量的总敏感度约为80%;②在近红外波段800~1100 nm范围内,平均叶倾角、叶片干物质含量和LAI是影响冠层反射率的3个最重要的参数;③在短波红外波段1100~2500 nm范围内,叶片含水量逐渐成为影响冠层反射率的主要参数。叶绿素、水和干物质等参数吸收系数的变化及相对大小的不同是造成以上变化的主要原因。该研究可以为植被生化参数的反演提供理论基础。     

利用多时相的高光谱航空图像监测冬小麦条锈病

冬小麦发生锈病 ,叶绿素被大量破坏 ,水分蒸滕量大大增加 ,叶片细胞大小、形态、叶片结构发生了改变 ,从而改变了叶片和冠层的光学特性 ,使得遥感探测与评价成为可能。利用多时相的高光谱航空飞行图像数据 ,了解、分析和发现条锈病病害对作物光谱的影响及其光谱特征 ;设计了病害光谱指数 ,成功地监测了冬小麦条锈病病害程度与范围。对比 3个生育期的条锈病与正常生长冬小麦的PHI图像光谱及光谱特征 ,发现 :5 6 0— 6 70nm黄边、红谷波段 ,条锈病病害冬小麦的冠层反射率高于正常生长的冬小麦光谱反射率 ;近红外波段 ,条锈病病害的冠层反射率低于正常生长的冬小麦光谱反射率 ;条锈病冬小麦冠层光谱红谷吸收深度和绿峰的反射峰高度都会减小     

Leaf to canopy upscaling approach affects the estimation of canopy traits

In remote sensing applications, leaf traits are often upscaled to canopy level using sunlit leaf samples collected from the upper canopy. The implicit assumption is that the top of canopy foliage material dominates canopy reflectance and the variability in leaf traits across the canopy is very small. However, the effect of different approaches of upscaling leaf traits to canopy level on model performance and estimation accuracy remains poorly understood. This is especially important in short or sparse canopies where foliage material from the lower canopy potentially contributes to the canopy reflectance. The principal aim of this study is to examine the effect of different approaches when upscaling leaf traits to canopy level on model performance and estimation accuracy using spectral measurements (in-situ canopy hyperspectral and simulated Sentinel-2 data) in short woody vegetation. To achieve this, we measured foliar nitrogen (N), leaf mass per area (LMA), foliar chlorophyll and carbon together with leaf area index (LAI) at three vertical canopy layers (lower, middle and upper) along the plant stem in a controlled laboratory environment. We then upscaled the leaf traits to canopy level by multiplying leaf traits by LAI based on different combinations of the three canopy layers. Concurrently, in-situ canopy reflectance was measured using an ASD FieldSpec-3 Pro FR spectrometer, and the canopy traits were related to in-situ spectral measurements using partial least square regression (PLSR). The PLSR models were cross-validated based on repeated k-fold, and the normalized root mean square errors (nRMSE_cv) obtained from each upscaling approach were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Results of the study showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error (nRMSE_cv < 0.2 for canopy N, LMA and carbon) as well as high explained variance (R² > 0.71) for both in-situ hyperspectral and simulated Sentinel-2 data. The widely-used upscaling approach that considers only leaf traits from the upper illuminated canopy layer yielded a relatively high error (nRMSE_cv>0.2) and lower explained variance (R² < 0.71) for canopy N, LMA and carbon. In contrast, canopy chlorophyll upscaled based on leaf samples collected from the upper canopy and total canopy LAI exhibited a more accurate relationship with spectral measurements compared with other upscaling approaches. Results of this study demonstrate that leaf to canopy upscaling approaches have a profound effect on canopy traits estimation for both in-situ hyperspectral measurements and simulated Sentinel-2 data in short woody vegetation. These findings have implications for field sampling protocols of leaf traits measurement as well as upscaling leaf traits to canopy level especially in short and less foliated vegetation where leaves from the lower canopy contribute to the canopy reflectance.     

Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels

Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L.) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R² of 0.74 and 0.94, respectively. Results showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780–800 nm) and either green (540–560 nm) or red-edge (730–750 nm) spectral bands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively.     

Estimating mineral content of indigenous browse species using laboratory spectroscopy and sentinel-2 imagery

Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R² = 0.72, p < 0.05) for winter and (R² = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R² = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (C_AB) and nitrogen (C_N) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that C_AB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate C_AB was achieved from laboratory spectra using continuum-removal transformed data (R²cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R²cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R²cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate C_AB were typically located in the red edge region and near the green reflectance peak. For C_N, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.     

柑橘植株冠层氮素和光合色素含量近地遥感估测

柑橘植株营养状况的遥感监测是实现果树轻简高效管理和优质丰产的重要手段,但迄今有关基于低空遥感信息的果树营养诊断研究鲜见报道。本文采用具有490 nm、550 nm、570 nm、671 nm、680 nm、700 nm、720 nm、800 nm、840 nm、900 nm、950 nm等11个波段光谱的八旋翼飞行器(UAV)载多光谱遥感系统,获取距地面100 m高度的哈姆林甜橙植株春季冠层近地遥感信息,对比分析基于多元散射校正(MSC)和标准正态变量(SNV)两种预处理光谱和原始光谱(OS)的偏最小二乘(PLS)、多元线性回归(MLR)、主成分回归(PCR)及最小二乘支持向量机(LS-SVM)等4种模型对冠层叶片氮素、叶绿素a、叶绿素b和类胡萝卜素含量预测精度的影响。结果显示,距地面100 m高度的多光谱信息,通过SNV光谱预处理和MLR建模对冠层叶片氮素、叶绿素a和叶绿素b含量的预测效果均较好,预测集相关系数(Rp)值分别达0.8036、0.8065和0.8107,预测均方根误差(RMSEP)值分别为0.1363、0.0427和0.0243;而在SNV光谱预处理基础上的LS-SVM建模对冠层类胡萝卜素含量预测效果更优,Rp值达到了0.8535,RMSEP值为0.0117。表明利用机载多光谱图像信息可实现对柑橘植株冠层全氮及叶绿素a、叶绿素b和类胡萝卜素含量的较好估算,为大规模柑橘园植株冠层营养状况的精准和高效监测提供了一条新途径。     

引黄灌区水稻叶面积指数的高光谱估测模型

水稻叶面积指数（leaf area index,LAI）是评价其长势的重要农学参数,高光谱遥感能够实现叶面积指数的快速无损监测。为了寻找反演水稻LAI的最优植被指数,扩展水稻LAI高光谱估测模型的普适性,选取宁夏引黄灌区水稻为研究对象,通过设置不同氮素处理,借助相关分析、回归分析等方法研究高光谱植被指数与水稻LAI之间的定量关系,并通过确立的最优波段组合,构建4种植被指数与水稻LAI的高光谱反演模型。结果表明,水稻LAI在抽穗末期达到最大值,并随氮素水平的增加而增加;水稻冠层原始光谱反射率在400~722 nm和1 990~2 090 nm波段与LAI达到极显著负相关水平,在近红外区域760~1 315 nm与LAI呈极显著正相关。模型检验结果表明,以比值植被指数RVI（_850,750）为变量建立的水稻LAI估测模型最佳,研究结果可为水稻LAI的高光谱估测提供地域参考。     

Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice

The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.     

Modelling the spectral response of the desert tree Prosopis tamarugo to water stress

In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm², while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm². The red-edge chlorophyll index (CI_red-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.     

基于PROSPECT＋SAIL模型的遥感叶面积指数反演

以PROSPECT+SAIL模型为基础，从物理机理角度反演植被叶面积指数(LAI)。首先，通过FLAASH模型进行大气校正，使得图像像元值表达植被冠层反射率； 然后，根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据，以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数，得到植被冠层反射率，将结果与遥感影像的植被冠层反射率对应，回归出植被LAI； 最后，以地面实测数据对遥感反演数据进行验证，并分析了误差的可能来源。