机载高光谱数据提取冬小麦冠层叶绿素含量的模型分析及验证

遥感提取叶绿素含量的方法是精准农业的重要研究方向之一,但是如何用冠层光谱数据有效地提取叶绿素含量仍然是一个难点。本文用光谱指数TCARI和OSAVI的组合建立提取冬小麦冠层叶绿素含量的关系式,并使用实验田获取的冬小麦冠层光谱以及与之同步的机载高光谱传感器OMIS数据进行了验证。通过误差分析讨论了该方法用于遥感高光谱数据时需要注意的问题,表明大气校正的精度,传感器的信噪比以及波段中心的漂移是模型反演精度的主要制约因素。     

基于植被指数的叶绿素密度遥感反演建模与适用性研究

利用遥感数据反演叶绿素密度是对作物长势进行评估的有效手段.本文利用实测冬小麦和夏玉米两种作物、不同生育期的冠层光谱和叶片叶绿素含量数据,收集了14种光谱指数,分析各种光谱指数的叶绿素密度遥感模型的精度.优选了其中的8种植被光谱指数,建立了植被指数与叶绿素密度之间的回归模型,并利用不同生育期小麦数据和玉米数据对各模型进行验证,分析评价它们对不同生育期、不同作物类型的适用性.研究发现:利用SRI、RVI I、R-M和MTCI 4种植被指数所建模型对冬小麦不同生育期数据适用性较好,各生育期冠层叶绿素密度反演相对误差优于27％.其中,MTCI模型对不同作物类型的适用性最好,冠层叶绿素密度反演相对误差优于35％.     

磷营养胁迫对冬小麦冠层光谱的影响

因为磷素重要的营养作用,其胁迫的存在影响冬小麦的正常生长。借助地面遥感仪器获取冬小麦在磷营养胁迫下的多个生育期里的冠层光谱数据并对其影响特征进行了分析。利用因子分析方法提取主因子与含有丰富信息的光谱变量,并结合极显著水平(0.01)的均值比较与检验过程考察了冬小麦冠层光谱,确定了对磷营养胁迫敏感的光谱波段:760nm,810nm和870nm与950nm,并在此基础上结合冬小麦对磷素的吸收利用特征选定了运用冠层光谱敏感波段反射率探测和区分磷营养胁迫的关键生育期:拔节期。结果同时表明,对冬小麦磷营养胁迫而言,近红外区间(760nm~1100nm)光谱反射特征的区分能力要强于可见光区。本文同时指出了研究与发展利用遥感技术进行营养胁迫监测的方法和着重点。     

归一化植被指数的土壤背景影响去除

植被冠层归一化植被指数(normalized difference vegetation index,NDVI)由于不同土壤背景的混入干扰,导致利用NDVI信息对作物长势监测等应用的有效性降低。以安徽省来安县小麦农田为研究区,以2种土壤类型(水稻土和黄褐土)背景下拔节期冬小麦为研究对象,采用实测小麦冠层光谱及叶面积指数(leaf area index,LAI)数据,利用传统的照相法求算植被覆盖度,基于混合光谱理论,提出2种NDVI土壤背景影响去除模型(NDVIT),对模型进行对比验证。研究结果表明:2种模型均可去除一定的土壤背景影响;采用信噪比的分析方法定量研究2种模型抵抗土壤噪声影响的能力,分析发现NDVI1T提取植被信息抗土壤噪声能力更佳;2种土壤背景影响去除模型和NDVI的拟合关系良好,相关关系R~2均达到0.9以上。     

不同施氮水平下的小麦冠层光谱特征及产量分析

分析了在不同氮肥施用水平下,小麦冠层的高光谱响应在几个生育期内的变化情况,以及它们与小麦产量之间的关系。采用微分技术处理了小麦冠层反射光谱,提高了其区分小麦氮素营养水平的灵敏性;利用F-检验及方差分析与相关分析,研究小麦氮素处理水平、冠层反射光谱及其衍生信息(光谱反射率的一阶微分数据、归一化植被指数)、小麦产量三者之间的相关关系。研究结果表明,一阶微分技术能够提高小麦冠层光谱数据对氮素营养水平的响应,光谱数据的衍生形式也可与小麦产量建立很好的回归方程。     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

利用航空成像光谱数据进行冬小麦产量预测

以国产成像光谱仪PHI(Pushbroom Hyperspectral Imaget)所获遥感影像数据为基础，根据田间冬小麦单产遥感研究试验数据建立了研究区不同时相冬小麦单产预测模型，实现了利用航空高光谱遥感数据对研究区小麦产量的整体预测；对试验区土壤氮素水平与不同时相冬小麦预测产量以及试验区实测产量进行了初步分析，分析结果显示：土壤氮素分布的差异性对小麦的产量有明显影响。     

冬小麦播期的卫星遥感及应用

播种日期对冬小麦生长发育、产量和品质形成均有一定的影响。利用2003年拔节期的Landsat TM卫星的NDVI数据．成功地监测了冬小麦的播种日期。提出了基于NDVI和播种日期的冬小麦的遥感估产的优化模型，并在抽穗期至乳熟期的3次生育期的遥感估产中得到了成功验证与应用。利用出粉率与播种日期的显相关特性，采用拔节期的Landsat TM卫星的NDVI数据，成功预测了小麦籽粒的出粉率。     

Study on Grain Quality Effecting Factors and MonitoringMethods by Using Hyperspectral Data in Winter Wheat

冬小麦品质的影响因素及高光谱遥感监测方法黄文江,王纪华,刘良云,赵春江,宋晓宇,马智宏(国家农业信息化工程技术研究中心,北京　100089)摘要:研究了小麦品质的分类及其构成因素与环境条件之间的关系,各品质因素之间的关系。运用相同栽培条件下不同品种品质指标间的关系和变化规律,研究了品种因素对小麦品质的影响程度以及品种因素与品质指标之间的相关性,得出相同环境条件下籽粒的蛋白质含量与湿面筋含量、沉降值、吸水率、形成时间和稳定时间之间存在极显著的相关性。并利用不同品种、不同肥水条件下的作物关键生育时期的生化参量与光谱指数进行分析,得出开花期冬小麦叶片的类胡萝卜素与叶绿素a的比值与结构不敏感植被指数(SIPI)之间存在极显著的正相关,决定系数达到0.7207,冬小麦体内的全氮含量与类胡萝卜素与叶绿素a的比值之间存在极显著的负相关,决定系数为0.7245,并通过分析开花期冠层生化组分与籽粒品质指标间的相关性,得出开花期叶片全氮与籽粒蛋白质、湿面筋、干面筋和沉降值之间存在极显著的正相关,表面运用开花期光谱指数来反演叶片全氮含量,进而用来预测预报籽粒品质是切实可行的。关　键　词:冬小麦;高光谱数据;籽粒品质;监测方法中图分类号:TP 79     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段，对农业生产管理具有重要意义。以安徽龙亢农场为研究区，收集了中高分辨率多源卫星遥感数据并进行了定量化处理，构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型，生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律，分析了不同品种冬小麦的长势情况，发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上，构建了基于归一化植被指数（NDVI）的冬小麦估产模型，结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算，据此绘制了龙亢农场2017年冬小麦产量遥感估算地图，产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测，为区域范围内农作物长势监测提供了一种有效途径。     

Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index

Remote sensing estimation of leaf chlorophyll content is of importance to crop nutrition diagnosis and yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels. This study analyses the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well-accepted approach of the combination of transformed chlorophyll absorption index (TCARI) and optimized soil-adjusted vegetation index (OSAVI). A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up-table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm^?2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure.     

Evaluating hyperspectral imaging of wetland vegetation as a tool for detecting estuarine nutrient enrichment

Nutrient enrichment and eutrophication are major concerns in many estuarine and wetland ecosystems, and the need is urgent for fast, efficient, and synoptic ways to detect and monitor nutrients in wetlands and other coastal systems across multiple spatial and temporal scales. We integrated three approaches in a multi-disciplinary evaluation of the potential for using hyperspectral imaging as a tool to assess nutrient enrichment and vegetation responses in tidal wetlands. For hyperspectral imaging to be an effective tool, spectral signatures must vary in ways correlated with water nutrient content either directly, or indirectly via such proxies as vegetation responses to elevated nitrogen. Working in Elkhorn Slough, central California, where intensive farming practices generate considerable runoff of fertilizers and pesticides, we looked first for long- and short-term trends among temporally ephemeral point data for nutrients and other water quality characters collected monthly at 18 water sampling stations since 1988. Second, we assessed responses of the dominant wetland plant, Salicornia virginica (common pickleweed) to two fertilizer regimes in 0.25 m² experimental plots, and measured changes in tissue composition (C, H, N), biomass, and spectral responses at leaf and at canopy scales. Third, we used HyMap hyperspectral imagery (126 bands; 15–19 nm spectral resolution; 2.5 m spatial resolution) for a synoptic assessment of the entire wetland ecosystem of Elkhorn Slough. We mapped monospecific Salicornia patches (～ 56–500 m²) on the ground adjacent to the 18 regular water sampling sites, and then located these patches in the hyperspectral imagery to correlate long-term responses of larger patches to water nutrient regimes. These were used as standards for correlating plant canopy spectral responses with nitrogen variation described by the water sampling program. There were consistent positive relationships between nitrogen levels and plant responses in both the field experiment and the landscape analyses. Two spectral indices, the Photochemical Reflectance Index (PRI) and Derivative Chlorophyll Index (DCI), were correlated significantly with water nutrients. We conclude that hyperspectral imagery can be used to detect nutrient enrichment across three spatial and at least two temporal scales, and suggest that more quantitative information could be extracted with further research and a greater understanding of physiological and physical mechanisms linking water chemistry, plant properties and spectral imaging characteristics.     

砂土麦田追施硫肥对冬小麦产量和质量的影响

在砂质土壤条件下,研究了追施硫肥对冬小麦产量和品质的影响。结果表明:拔节期追施硫肥显著提高小麦子粒产量,千粒重随着追施硫肥用量的增多而提高,不同追硫处理与对照相比均达到差异极显著水平;追施硫肥显著提高了小麦子粒容重和子粒硬度,改善了小麦的磨粉品质和面团流变学特性,尤其对小麦的淀粉糊化特性指标影响较大,高峰粘度、低谷粘度、最终粘度、稀懈值与对照相比均达到极显著水平。比较不同硫肥追施量对产量和品质的调控作用以及投入成本来看,以拔节期每公顷追施30kg纯S的效果较好。     

基于遥感数据和作物生长模型的小麦变量施肥研究进展

就国内外基于遥感数据和作物生长模型在变量施肥技术的研究应用作了阐述, 提出了快速、无损农业测试技术将是精准变量农业和数字农业今后的发展方向, 对作物生长模型以及精准变量施肥技术的研究进展作了较系统的调查研究, 阐述了将遥感数据与作物生长模型进行数据同化, 实现以高产、优质、环保为目的农业生产的可行性。并结合我国国情提出了发展精准农业变量施肥技术所面临的困难和出路。     

Uncertainties in fPAR estimation of grass canopies under different stress situations and differences in architecture

The fraction of intercepted photosynthetic active radiation (fPAR) is a key variable used by the Monteith model to estimate the net primary productivity (NPP). This variable can be assessed by vegetation indices (VIs) derived from spectral remote sensing data but several factors usually affect their relationship. The objectives of this work were to analyse the fPAR dynamics and to describe the relationships between fPAR and several indices (normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), Green NDVI (GNDVI), visible atmospherically resistant index (VARI) green, VIgreen and red edge position (REP)) under different water and nutrient treatments for two species with different canopy architectures. Two C₃ grass species with differences in leaf orientation (planophile and erectophile) were cultivated from seeds in pots. Four treatments were applied combining water and nitrogen availability. Every week, canopy reflectance and fPAR were measured. Aerial biomass was clipped to estimate final above-ground production for each species and treatment. Starting from reflectance values, the indices were calculated. Planophile species have a steeper (but not significantly) slope in VIs–fPAR relationships than the erectophile species. Water and nutrient deficiencies treatment showed no relationship with fPAR in any spectral index in the erectophile species. In the other species, this treatment showed significant relationship according to the index used. Analysing each species individually, treatments did not modify slopes except in one case (planophile species between both treatments with high nitrogen but differing in water availability). Among indices, GNDVI was the best estimator of fPAR for both species, followed by NDVI and OSAVI. Inaccurate results may be obtained from commonly reported spectral relationships if plants' stress factors are not taken into account.     

Assessment of water-stress effects on crops

The present paper describes the results of a ground experiment conducted on major crops of India during December 1980 to April 1981 and October 1981 to April 1982. The crops selected were wheat, chickpea and mustard. The experiment was jointly conducted between the Indian Space Research Organization (ISRO) and the Indian Agricultural Research Institute. Canopy spectral reflectance and physiological variables were measured over the complete crop cycle. The results of the 2 year experiment led to the following conclusions: (i) spectral parameters can be used for detecting water and nutrient stress in crops, (ii) spectral parameters are highly correlated to physiological variables and final production, (iii) correlation is further improved by including the effect of weather parameters, (iv) stress degree day is highly correlated to grain yield and (v) a yield model based on photosynthesis calculation worked well for the wheat experiment.     

TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。