共查询到16条相似文献，搜索用时 500 毫秒
1.
利用光谱反射率估算叶片生化组分和籽粒品质指标研究 总被引：2，自引：0，他引：2
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。 相似文献
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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 总被引：3，自引：0，他引：3
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。 相似文献
3.
利用高光谱遥感图像估算小麦氮含量 总被引：29，自引：0，他引：29
利用2001—04—26实用型模块化成像光谱仪(0MIS)在北京小汤山地区获取的航空高光谱遥感图像，对图像进行了精确的几何纠正和反射率转换，提取出43条小麦图像光谱与地面叶片全氮含量数据相对应，运用红边、光谱吸收特征分析方法和逐步回归算法，选择和设计了叶片全氮反演的特征波段和特征参数，并进行了全氮含量境图。实验结果表明：由吸收特征光谱(590-756nm，1096—1295nm，1295—1642nm)确定的特征深度与面积能够很好地对叶片全氮含量进行反演；NDVI(NRCA1175.8，NRCA733.9)和NDVI(dr745，dr699．2)与TN的关系最好(R^2分别为0．8145，0．769)；全氮含量填图的值域和分布与地面调查和测量结果一致。 相似文献
4.
苹果树叶片全氮含量高光谱估算模型研究 总被引：5，自引：1，他引：4
采用Field-Spec Pro便携式光谱仪,测定了不同苹果品种叶片的光谱反射率及对应的全氮含量,采用相关性及单变量线性与非线性拟合分析技术,对全氮含量与原始光谱反射率、一阶微分光谱、高光谱参数之间的关系进行了分析。研究确立叶片全氮含量的定量监测模型,以期为遥感技术在苹果氮素营养诊断中的实际应用提供理论依据。结果表明:全氮含量与原始光谱在715nm处具有最大负相关系数(r=?0.817),并且基于此波长所构建的对数关系估算模型明显优于线性模型;光谱反射率一阶微分值在723nm处具有最大正相关系数(r=0.87),并且基于此波长所构建的线性和非线性模型的拟合效果接近;对于所选取的3类高光谱特征变量,全氮含量除了与黄边位置及由红边位置和黄边面积所构建的比值植被指数和归一化植被指数的相关性较弱外,与其余变量均呈极显著相关,说明这些变量对苹果叶片全氮含量进行估算具有可行性。对所建立的各类方程进行检验,最终筛选确定在723nm处的光谱反射率一阶微分值所构建的指数模型作为苹果叶片全氮含量的预测模型最为理想。 相似文献
5.
以福建省平和县琯溪蜜柚为研究对象，利用星载Hyperion高光谱遥感数据对蜜柚叶片进行氮浓度估测。在分析Hyperion数据特征的基础上进行大气校正、几何纠正等预处理，从而得到图像反射率；结合地面光谱测量和蜜柚叶片采样分析，通过逐步回归分析法研究叶片氮浓度与高光谱图像反射率及其衍生量的关系，最终建立其遥感定量监测模型。结果表明，图像反射率的对数变换更有利于氮浓度的定量反演，入选的波段是983 nm、1 245 nm、1 316 nm和1 457 nm，其中1 245 nm波段对氮浓度影响最大，1 457 nm波段最小。利用该模型对氮浓度进行估算的值域与地面调查结果一致，说明利用高光谱进行氮浓度定量反演具有一定的可行性。 相似文献
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7.
冬小麦冠层氮素的垂直分布及光谱响应 总被引：23，自引：2，他引：23
考察了田间条件下冬小麦主要生育阶段冠层氮素、叶绿素的垂直分布及其光谱响应。不同叶层的叶片含氮量按上 (冠层顶部向下至 1 / 3株高处 )、中、下层的顺序呈明显下降的梯度 ,全生育期不同土壤施氮处理平均 ,上、中层间相差 1 3 3% ,中、下层间相差 2 9 5 %。在生育前期 ,各层叶片的含氮量随土壤供氮水平增高而增加 ,但不同叶层间氮素的梯度相对稳定。到生育中后期 ,中、下层叶片间氮素含量梯度增大 ,且随土壤供氮水平增高而加剧 ,最大时可相差 4 5 3% ;冠层内叶绿素 (a b)含量的垂直分布规律与氮素含量的垂直分布相类似 ,但对土壤供氮水平的反应上表现出与氮素不尽一致的趋势。不同叶层的光谱特征表现为 ,在土壤低氮水平下 ,不同叶层间在红光波段、短波红外波段 (1 4 0 0nm— 1 80 0nm及 1 95 0nm— 2 30 0nm)的反射率差异显著 ,下部叶层的反射率显著高于上、中叶层 ,但在土壤高氮水平下 ,上述差异消失 ;在近红外平台处 ,不同叶层间反射率按上、中、下顺序降低 ,梯度分布特征明显。利用近红外波段的冠层反射光谱能够很好地反演中下层叶片的叶绿素含量 相似文献
8.
叶片作为植物冠层的基本组成元素,其自身的光学特性直接影响着遥感所能获得的植物冠层反射光谱。从原理上讲,叶片的光学特性不仅取决于其内部生化组分含量的多少,还与其物理结构密切相关。因此对叶片内部物理结构进行估算有助于分离其对叶片光谱的影响,从而提高叶片生化信息反演的精度。在基于叶片内部辐射传输过程的PROSPECT模型中,叶片内部结构用一个假想的叶肉结构参数N来描述。PROSPECT模型模拟光谱发现,N对叶片反射率和透过率均影响显著,且影响范围涵盖400—2500nm的全部波段。本文利用水稻叶片实测光谱和生化数据尝试了3种N的估算方法,包括两种经验方法和一种模型反演方法,并对其进行比较。结果表明,由于两种经验方法都基于N和表观叶面积（SLA）之间的非线性经验公式,因此两者具有内在的数学关系。运用模型反演方法估算的N可在实测水稻光谱和模型模拟光谱间得到最小RMSE,且其在数值上小于两种经验方法的估算值。以N为因变量,叶片光谱反射率为自变量,运用逐步线性回归分析建立了N的光谱估算模型,550nm,816nm,1210nm和1722nm四个波段被选入模型,回归效果较好,为N的估算提供了一种新的经验方法。 相似文献
9.
基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究 总被引：6，自引：0，他引：6
利用ASD便携式野外光谱仪和SPAD-502叶绿素计实测了落叶阔叶树法国梧桐叶片的高光谱反射率与叶片绿度,并对原始光谱反射率及一阶导数光谱与叶片绿度进行了相关分析;综合分析了10个常见光谱植被指数与法国梧桐叶绿素含量的相关性与预测性;最后利用主成分分析对光谱数据进行降维,将得到的主成分得分作为BP人工神经网络模型的输入变量进行了法国梧桐叶绿素含量的估算。结果表明:法国梧桐的叶片反射光谱数据与叶绿素含量的相关性在可见光区域显著,导数光谱数据在绿黄光区和红光区的部分波段与叶绿素含量的相关系数大于对应波段光谱反射率与叶绿素含量的相关关系。在所列举的10个常用植被指数中归一化植被指数与叶绿素含量的关系最密切,相关系数达到了0.7957。主成分分析的BP神经网络模型可以容纳更多的波段信息进行叶绿素含量的估算,预测值与实测值之间的线性回归的确定性系数R2为0.9883,是一种良好的植被叶绿素含量高光谱反演模式。 相似文献
10.
冠层反射光谱对植被理化参数的全局敏感性分析 总被引：1，自引：0，他引：1
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。 相似文献
11.
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, VIgreen, 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. 相似文献
12.
Mahender Singh Ram Niwas M. L. Khichar Manoj K. Yadav 《Journal of the Indian Society of Remote Sensing》2006,34(1):1-5
Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology,
CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on
25th November, 10th and 25th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at
anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves
were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral
indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness
index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown
crop with maximum dose of nitrogen (180 kg N ha-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded
during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for
chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R2’ values of these models ranged between 0.87 and 0.95. 相似文献
13.
Y. Nagaraja Rao Y. V. Subbarao K. S. Sundara Sarma R. L. Karale K. K. Narula Naveen Kalra 《Journal of the Indian Society of Remote Sensing》1987,15(1):41-47
Maize crop was sown at weekly intervals on six dates in a randomized replicated trial under nonlimiting moisture conditions. The different dates of sowing represent different growth stages in the same given environment. Spectral data were collected using a portable radiometer at different wavelengths, ranging form visible to infrared on two different dates. The spectral reflectance data in the red and infrared region were analysed for their sensitivity to leaf area index and leaf dry biomass. During active crop growth period significant correlations existed between leaf area index and ratio of infrared to red as well as the normalized differences. Similar relationships were also observed between dry biomass and spectral data. However, these relationships were found to be valid upto the crop growth stage when the leaf area index has reached its maximum, corresponding to flowering. Beyond this stage, the spectral reflectances were found to be not related to LAI. The relsults suggest the possibility of obtaining crop phenological information from the spectral response data. 相似文献
14.
S. Pradhan K. K. Bandyopadhyay R. N. Sahoo V. K. Sehgal R. Singh V. K. Gupta D. K. Joshi 《Journal of the Indian Society of Remote Sensing》2014,42(4):711-718
Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha?1 (15 %) for grain yield and 1609 kg ha?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat. 相似文献
15.
Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring. 相似文献
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地上生物量能够有效反映作物的生长状态，其信息的实时估算对产量预测和农田生产管理都有重要意义。作物生长模型因其详尽的生理生化基础和对生长过程数字化描述能力，成为生物量估算的理想模型。近年来，研究人员利用数据同化算法将时间序列遥感数据同化到作物生长模型中，实现了作物模型由基于气象站的点模拟到区域尺度面模拟的外推，使生物量模拟结果同时具备大范围和机理性两个方面的特点。这一模式下，时间序列的遥感数据质量将对生物量模拟精度产生直接影响，作物生长后期受到光谱饱和的影响，遥感数据的作物冠层信息获取能力会出现明显下降，因此有必要对该阶段遥感数据和作物模型的结合方式进行优化，提升生物量模拟精度。本文针对东北地区春玉米生物量遥感估算存在的问题，提出了利用WOFOST作物模型结合无人机（UAV）遥感数据实现作物生长后期生物量准确估算的新思路。新思路首先利用多光谱遥感数据获取WOFOST模型具备较高空间异质性的土壤速效养分参数以提升模型的空间信息模拟能力，使其能在一定程度上摆脱点尺度模拟的限制。同时，结合集合卡尔曼滤波算法将生长前期无人机（UAV）遥感数据同化到模型中，以缩短模型单独运行时间，减少模型运行过程中的参数误差累积，实现无遥感数据参与下的短期作物生长模拟，并输出生长后期相应的生物量模拟结果。最后，本文利用地面实测数据对新方法的生物量模拟精度进行了评价。结果表明，与全生育期数据同化相比，新方法的生物量估算精度有了明显的提升（全生育期同化：R2 = 0.45，RMSE = 4254.30 kg/ha；新方法：R2= 0.86，RMSE = 2216.79 kg/ha）。 相似文献