The leaf area index（LAI） is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop cano...The leaf area index（LAI） is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types：approaches based on statistical models,physical models（i.e.,canopy reflectance models）,and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches（e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models）.Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the ＂curse of dimensionality＂ and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.展开更多
The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were ...The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.展开更多
叶面积指数(leave area index,LAI)是表征植被冠层结构和生长状况的关键参数,采用遥感技术进行LAI反演是遥感反演领域的热点和难点之一。利用小麦关键生育期的高光谱数据,计算其一阶和二阶导数,并构建植被指数(RVI,NDVI,EVI,DVI和MSAVI...叶面积指数(leave area index,LAI)是表征植被冠层结构和生长状况的关键参数,采用遥感技术进行LAI反演是遥感反演领域的热点和难点之一。利用小麦关键生育期的高光谱数据,计算其一阶和二阶导数,并构建植被指数(RVI,NDVI,EVI,DVI和MSAVI)及三边变量参数等高光谱变量;将上述参数与小麦LAI数据进行相关性分析,并利用交叉验证法进行多种回归分析,确定反演小麦LAI的敏感参数,选择反演模型;最后使用敏感参数构建所有样本的小麦LAI反演模型,并比较其拟合效果。研究结果表明:经过交叉验证的反演建模,其拟合结果的均方根误差(RMSE)整体上较未经交叉验证反演建模结果的RMSE小;在用敏感参数构建的回归模型中,RVI立方回归模型是用遥感数据反演小麦LAI的最优模型。展开更多
植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光...植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光谱分辨率监测植物的光谱特征,为精准反演LAI提供了基础。本项研究以武夷山国家公园黄岗山顶的亚高山草甸为研究对象,通过建立多种高光谱植被指数和拟合多光谱植被指数反演叶面积指数的统计模型,并比较高光谱与多光谱对叶面积指数反演的效果,阐明用于反演高覆盖率亚高山草甸的最适高光谱和拟合多光谱植被指数。结果表明:高光谱新植被指数(NVI)对于反演LAI有最好的效果,R^(2)=0.85,P<0.01;依据高光谱NVI拟合而成的多光谱NVI反演结果次之,R^(2)=0.82,P<0.01。几种常用比值植被指数NDVI、MSR、RVI和GNDVI在高光谱和拟合多光谱反演结果中相差不大,表现较好,R^(2)都在0.65以上。通过对比高光谱和拟合Sentinel-2A和Landsat-8两种多光谱卫星波段的反演结果发现,光谱响应函数中具有更窄波段范围的近红外、红、绿、蓝波段构成的植被指数可以得到更好的反演结果,而固定波段的高光谱植被指数未必在每种植被指数中都具有最好的反演效果。同时,发现当某种植被指数反演LAI的线性回归方程的斜率越大,说明这种植被指数越有可能随LAI的增大而出现饱和现象,相反的,斜率越小则说明该种植被指数没有出现饱和现象。此外,在研究区内使用高光谱和拟合多光谱波段植被指数法反演LAI,NDVI都获得了较好的效果,存在很好的线性关系,之前的很多研究和判断都认为NDVI不适用于反演高覆盖植被的LAI,这个发现是具有意义的,表明高覆盖植被的叶面积指数在一定范围内是能够被NDVI(应用最广泛的植被指数)较好的反演,进一步扩展了NDVI反演LAI的适用性和可能性。展开更多
叶面积指数(leaf area index,LAI)是生态系统的重要结构参数,可以反映植物冠层结构、植物群落生命活力及其环境效应。该研究以兴安落叶松林为研究对象,基于高分一号遥感影像,通过计算4种植被指数(NDVI、RVI、DVI和OSAVI),结合实测LAI数...叶面积指数(leaf area index,LAI)是生态系统的重要结构参数,可以反映植物冠层结构、植物群落生命活力及其环境效应。该研究以兴安落叶松林为研究对象,基于高分一号遥感影像,通过计算4种植被指数(NDVI、RVI、DVI和OSAVI),结合实测LAI数据,建立LAI的统计回归模型,筛选出兴安落叶松林LAI的最优遥感反演模型。结果表明:研究区兴安落叶松林LAI与4种植被指数之间均有较强的相关性,其中基于OSAVI的LAI线性模型反演精度最高。表明该文建立的LAI经验统计模型具有较高的精度,利用GF-1影像可以快速、大面积反演兴安落叶松林的LAI,研究区兴安落叶松林长势较好,LAI基本大于3,该研究结果可为利用经验统计模型反演林分LAI提供参考。展开更多
以高光谱遥感技术实现了小麦叶面积指数(leaf area index,LAI)的反演。对18种高光谱指数进行了比较分析,筛选出了可敏感反映小麦LAI的高光谱指数OSAVI,并以地面光谱数据为样本建立了小麦LAI的反演模型。分析表明,指数OSAVI所建立的反演...以高光谱遥感技术实现了小麦叶面积指数(leaf area index,LAI)的反演。对18种高光谱指数进行了比较分析,筛选出了可敏感反映小麦LAI的高光谱指数OSAVI,并以地面光谱数据为样本建立了小麦LAI的反演模型。分析表明,指数OSAVI所建立的反演模型校正集与预测集R2分别达0.823与0.818,在各指数中反演精度最高。利用反演模型逐象元对OMIS影像进行解算,实现小麦LAI的空间量化表达,并将反演结果与地面实测值进行回归拟合,发现两组数据的拟合模型R2达0.756,RMSE为0.500,具有较高的相似度。结果表明:以高光谱指数进行小麦LAI的反演是可行的,且OSAVI为优选指数。展开更多
获取农作物叶面积指数(leaf area index,LAI)及其动态变化对于农作物长势监测和产量估测等应用具有重要的意义。基于冠层反射率模型(物理模型)的LAI遥感反演方法具有良好的普适性,对地面数据依赖较少,近年来广泛应用于农作物LAI高光谱...获取农作物叶面积指数(leaf area index,LAI)及其动态变化对于农作物长势监测和产量估测等应用具有重要的意义。基于冠层反射率模型(物理模型)的LAI遥感反演方法具有良好的普适性,对地面数据依赖较少,近年来广泛应用于农作物LAI高光谱反演研究。然而,当物理模型参数取值尽可能准确(代入参数实测值或依据先验知识取值)时,模拟光谱与实测光谱间仍然存在误差,研究称之为"光谱模拟误差"。该研究通过比对实测冬小麦冠层光谱与ACRM(a two-layer canopy reflectance model)模型最优模拟光谱,展示了光谱模拟误差在各波段、不同样本点的分布规律。据此,根据对光谱模拟误差与高光谱数据降维的不同考虑,制订了4种LAI反演波段选择方案。通过对比基于不同波段选择方案的LAI反演精度,分析了光谱模拟误差对LAI反演的影响;讨论了综合考虑高光谱数据降维与光谱模拟误差的LAI反演波段选择方法。通过合理的波段选择,限制了光谱模拟误差的影响,提高了LAI反演精度。该研究结果有助于探索合理的LAI高光谱反演波段选择方法,为合理利用高光谱数据反演农作物LAI提供科学参考。展开更多
基金financed by the National High-Tech R&D Program of China(2012AA12A304)the National Natural Science Foundation of China(41271112 and 41201089)
文摘The leaf area index（LAI） is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types：approaches based on statistical models,physical models（i.e.,canopy reflectance models）,and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches（e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models）.Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the ＂curse of dimensionality＂ and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.
基金European Com mission Project, No.ICA 4-CT-2002-10004 N ational Natural Science Foundation of China, N o. 40371081 K now ledge Innovation ProjectofCA S,N o.K ZCX 3-SW -146
文摘The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of TM3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.
基金supported by the National Natural Science Foundation of China (No. 40571115)the Hi-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Basic Research Program (973) of China (No. 2006BAD10A09)
文摘叶面积指数(leave area index,LAI)是表征植被冠层结构和生长状况的关键参数,采用遥感技术进行LAI反演是遥感反演领域的热点和难点之一。利用小麦关键生育期的高光谱数据,计算其一阶和二阶导数,并构建植被指数(RVI,NDVI,EVI,DVI和MSAVI)及三边变量参数等高光谱变量;将上述参数与小麦LAI数据进行相关性分析,并利用交叉验证法进行多种回归分析,确定反演小麦LAI的敏感参数,选择反演模型;最后使用敏感参数构建所有样本的小麦LAI反演模型,并比较其拟合效果。研究结果表明:经过交叉验证的反演建模,其拟合结果的均方根误差(RMSE)整体上较未经交叉验证反演建模结果的RMSE小;在用敏感参数构建的回归模型中,RVI立方回归模型是用遥感数据反演小麦LAI的最优模型。
文摘植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光谱分辨率监测植物的光谱特征,为精准反演LAI提供了基础。本项研究以武夷山国家公园黄岗山顶的亚高山草甸为研究对象,通过建立多种高光谱植被指数和拟合多光谱植被指数反演叶面积指数的统计模型,并比较高光谱与多光谱对叶面积指数反演的效果,阐明用于反演高覆盖率亚高山草甸的最适高光谱和拟合多光谱植被指数。结果表明:高光谱新植被指数(NVI)对于反演LAI有最好的效果,R^(2)=0.85,P<0.01;依据高光谱NVI拟合而成的多光谱NVI反演结果次之,R^(2)=0.82,P<0.01。几种常用比值植被指数NDVI、MSR、RVI和GNDVI在高光谱和拟合多光谱反演结果中相差不大,表现较好,R^(2)都在0.65以上。通过对比高光谱和拟合Sentinel-2A和Landsat-8两种多光谱卫星波段的反演结果发现,光谱响应函数中具有更窄波段范围的近红外、红、绿、蓝波段构成的植被指数可以得到更好的反演结果,而固定波段的高光谱植被指数未必在每种植被指数中都具有最好的反演效果。同时,发现当某种植被指数反演LAI的线性回归方程的斜率越大,说明这种植被指数越有可能随LAI的增大而出现饱和现象,相反的,斜率越小则说明该种植被指数没有出现饱和现象。此外,在研究区内使用高光谱和拟合多光谱波段植被指数法反演LAI,NDVI都获得了较好的效果,存在很好的线性关系,之前的很多研究和判断都认为NDVI不适用于反演高覆盖植被的LAI,这个发现是具有意义的,表明高覆盖植被的叶面积指数在一定范围内是能够被NDVI(应用最广泛的植被指数)较好的反演,进一步扩展了NDVI反演LAI的适用性和可能性。
文摘叶面积指数(leaf area index,LAI)是生态系统的重要结构参数,可以反映植物冠层结构、植物群落生命活力及其环境效应。该研究以兴安落叶松林为研究对象,基于高分一号遥感影像,通过计算4种植被指数(NDVI、RVI、DVI和OSAVI),结合实测LAI数据,建立LAI的统计回归模型,筛选出兴安落叶松林LAI的最优遥感反演模型。结果表明:研究区兴安落叶松林LAI与4种植被指数之间均有较强的相关性,其中基于OSAVI的LAI线性模型反演精度最高。表明该文建立的LAI经验统计模型具有较高的精度,利用GF-1影像可以快速、大面积反演兴安落叶松林的LAI,研究区兴安落叶松林长势较好,LAI基本大于3,该研究结果可为利用经验统计模型反演林分LAI提供参考。
文摘以高光谱遥感技术实现了小麦叶面积指数(leaf area index,LAI)的反演。对18种高光谱指数进行了比较分析,筛选出了可敏感反映小麦LAI的高光谱指数OSAVI,并以地面光谱数据为样本建立了小麦LAI的反演模型。分析表明,指数OSAVI所建立的反演模型校正集与预测集R2分别达0.823与0.818,在各指数中反演精度最高。利用反演模型逐象元对OMIS影像进行解算,实现小麦LAI的空间量化表达,并将反演结果与地面实测值进行回归拟合,发现两组数据的拟合模型R2达0.756,RMSE为0.500,具有较高的相似度。结果表明:以高光谱指数进行小麦LAI的反演是可行的,且OSAVI为优选指数。
