羊草草甸草原FPAR时间变化规律分析

FPAR是植被叶子在光合有效辐射(400～700nm)波段有多少太阳光能被吸收的一个度量,表示了植被冠层能量的吸收能力,是描述植被结构以及与之相关的物质与能量交换过程的基本生理变量.FPAR模型是否能真实反映植被冠层吸收光合有效辐射状况,将直接影响遥感估算草地NPP的不确定性程度.本文通过对长生季羊草草甸草原冠层PAR各分量的观测,研究了入射PAR、冠层反射PAR及透过冠层到达地面的PAR的时间变化规律,并以观测的PAR为基础计算分析了羊草草甸草原FPAR的时间交化规律.结果表明,羊草草甸草原入射PAR及透射PAR日交化规律明显,呈较标准的正弦曲线变化;晴天FPAR的日变化呈较标准的余弦曲线交化,FPAR在早晚值较高,最高值约0.81,日平均FPAR值可以用9:30/太阳天顶角为48°时瞬时的FPAR值或14:30/太阳天顶角为30°时瞬时的FPAR值表示.     

基于HJ-1CCD影像的呼伦贝尔草原区MODIS/FPAR产品验证与分析

植物吸收性光合有效辐射分量(FPAR)是描述植被结构以及与之相关的物质与能量交换过程的基本生理变量。选取内蒙古呼伦贝尔谢尔塔拉镇针茅和羊草草甸草原为研究对象,利用2013年5次地面实测实验,通过HJ-I CCD高分辨率卫星影像建立统计模型,从而实现对该研究区MODIS/FPAR产品的验证与分析。1km HJ/FPAR"真值"与MODIS/FPAR产品值的变化趋势基本一致,并且二者具有较好的一致性,R2达到了0.6762。MODIS/FPAR产品能够很好地反映呼伦贝尔草甸草原在整个生长季的长势及物候变化,这是因为研究区样地尺度上HJ/FPAR和MODIS/FPAR产品值不但变化趋势相一致,而且相关系数R2高达0.9148。无论在同尺度(1km)还是整个研究区尺度,MODIS/FPAR均有一定的高估现象。研究结果对了解和进一步使用该地区的MODIS/FPAR产品具有重要的指导意义。     

基于ETM和图像融合的优势植被冠层叶面积指数和消光系数的遥感反演

叶面积指数和消光系数是表征植被群体冠层结构及光能利用的地球表层下垫面参量,国内外对叶面积指数的遥感反演有较多的研究与应用,但对消光系数的遥感反演尚不多见。我国南方少见单一大面积的均匀植被分布。为更好地匹配叶面积指数和光合有效辐射(用于估算消光系数)的实测数据,反映植被混交和疏密不均的状态,以Landsat ETM作为遥感信息源,通过HSV、Brovey和Gram-Schmidt(GS)3种图像融合方法的比较,选取效果最佳的图像融合方法,将ETM融合成空间分辨率为15 m的多光谱数据。以鄱阳湖源头梅江流域为研究区,在实测优势植被叶面积指数和光合有效辐射的基础上,利用植被指数法经验公式法反演流域的叶面积指数,并根据Beer-Lambert定律,建立了流域优势植被冠层消光系数的反演模型。在此基础上,反演了流域植被冠层叶面积指数和消光系数的空间分布,为SWAT植物生长模式的修正提供输入数据基础。     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用,近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型,充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像,从而实现在同一模型中多种遥感数据的模拟。目前,LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据,可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用,近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型,充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像,从而实现在同一模型中多种遥感数据的模拟。目前,LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据,可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用，近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型，充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像，从而实现在同一模型中多种遥感数据的模拟。目前，LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据，可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

结合GLAS和TM卫星数据的江西省森林高度和生物量制图

针对光学遥感受云雨天气的影响,并存在植被指数饱和、穿透性差而难以到达森林冠层以下等问题,不能有效反映植被垂直结构信息,难以准确地反演森林地上生物量,以大光斑激光雷达GLAS数据、Landsat TM光学遥感影像数据以及野外实测数据为数据源,建立了江西省森林的平均冠层高度模型和森林生物量模型。结果表明:GLAS数据提取出波形特征参数、ASTER GDEM数据提取出地形特征参数与实测树高数据建立森林冠层高度模型,获取离散的林冠高度,可以较好消除地形对GLAS波形的影响;通过建立Landsat TM数据计算的NDVI与离散林冠高度之间的关系,可以进行大尺度连续森林冠层高度的制图;并利用林冠高度与森林生物量之间的幂函数关系估算森林生物量。因此,大光斑激光雷达GLAS数据与光学遥感数据联合,能充分发挥多源遥感的优势,实现连续冠层高度和森林生物量的反演。     

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％.     

基于光谱指数的植被含水率遥感反演模型研究——以岷江上游毛尔盖地区为例

利用研究区植被样本实测含水率和实测光谱数据,基于植被光谱指数法,建立植被含水率与植被光谱指数之间的数学模型,同时利用该模型对研究区的遥感数据进行分析,反演植被含水率。结果证明:简单比值光谱指数与植被含水率有较好的相关性,线性模型更适合该研究区的植被含水率反演。1999年和2007年两年的植被含水率反演结果显示:9年间植被含水率提高,含水率高的面积增大。     

基于数据机理的植被叶面积指数遥感反演研究

定量获取地表植被高精度时序及空间覆盖的叶面积指数（Leaf Area Index, LAI）是生态监测及农业生产应用的重要研究内容。通过使用Moderate Resolution Imaging Spectroradiometer（MODIS）植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析。研究使用基于辐射传输理论的RossThick LiSparse Reciprocal（RTLSR）核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot（SAILH）模型进行植被冠层辐射特征的提取,使用Anisotropic Index （ANIX）异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function（BRDF）的辅助特征信息,发展了基于数据机理（Data-Based Mechanistic, DBM）的植被叶面积指数建模和估算方法。通过必要的林地、农作物、草地植被实验区反演及数值分析可得知：①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度。②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征。经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标。     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).     

Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain

Canopy phenology is an important factor driving seasonal patterns of water and carbon exchange between land surface and atmosphere. Recent developments of real-time global satellite products (e.g., MODIS) provide the potential to assimilate dynamic canopy measurements with spatially distributed process-based ecohydrological models. However, global satellite products usually are provided with relatively coarse spatial resolutions, averaging out important spatial heterogeneity of both terrain and vegetation. Therefore, bias can result from lumped representation of ecological and hydrological processes especially in topographically complex terrain. Successful downscaling of canopy phenology to high spatial resolution would be indispensable for catchment-scale distributed ecohydrological modeling, aiming at understanding complex patterns of water, carbon and nutrient cycling in mountainous watersheds. Two downscaling approaches are developed in this study to overcome this issue by fusing multi-temporal MODIS and Landsat TM data in conjunction with topographic information to estimate high spatio-temporal resolution biophysical parameters over complex terrain. MODIS FPAR (fraction of absorbed photosynthetically active radiation) is used to provide medium spatial resolution phenology, while the variability of vegetation within a MODIS pixel is characterized by Landsat NDVI. The algorithms depend on the scale-invariant linear relationship between FPAR and NDVI, which is verified in this study. Downscaled vegetation dynamics are successfully validated both temporally and spatially with ground-based continuous FPAR and leaf area index measurements. Topographic correction during the downscaling process has a limited effect on downscaled FPAR products except for the period around the winter solstice in the study area.     

青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

基于MODIS数据的玉米植被参数估算方法的对比分析

基于实测数据建立了FPAR、LAI的植被指数估算模型（NDVI、RVI、NDWI）,并将其应用于MODIS BRDF数据对德惠地区玉米FPAR、LAI进行估算,然后将MODIS 15A2 FPAR/LAI产品值分别与BRDF估算值、地面实测值进行对比分析。主要得出以下结论：植被指数NDVI、RVI都能较好地用于实测数据和MODIS BRDF数据的FPAR、LAI估算;NDWI虽然在实测数据中估算玉米FPAR、LAI的效果优于NDVI、RVI,但其应用于MODIS BRDF数据估算FPAR、LAI时,效果却较差。BRDF数据估算FPAR与MODIS 15A2 FPAR值的关系因生长时期不同而异,在玉米生长前期,前者高于后者,而生长后期两者却较相近;BRDF估算LAI值一直都高于MODIS 15A2 LAI产品值。生长季前期,MOD15A2 FPAR、LAI值接近实测值,而在后期却高于实测值。通过分析也表明,玉米苗期MODIS 15A2 FPAR数值变化范围较小,产品算法对实际FPAR变化尚不够敏感,这可能是影响MODIS FPAR产品精度的一个原因。     

Analysis of the Spatio-temporal Variation in FPAR of the Tibetan Plateau from 1982 to 2015

The absorbed and utilized Fraction of Photosynthetically Active Radiation(FPAR) reflects the capacity of carbon fixation and oxygen release by vegetation, which may vary over space and time in large scale. Analysis of spatial-temporal variation in FPAR is an important topic of plant ecology. Based on GIMMS NDVI3g (1982~2015) and MODIS NDVI (2001~2015) data in the Tibetan plateau, here we used the nonlinear, semi-theoretical and semi-empirical models to inverse and analyze the spatial and temporal variation in FPAR. The results showed that (1) The spatial distributions of FPAR derived from GIMMS NDVI3g and MODIS NDVI were highly consistent, with the correlation coefficient being 0.82 (P<0.01). The area in which the trends of inter-annual change in the two inversion data were consistent for at least 6 years made up 80% of the studying area. (2) FPAR in Tibetan Plateau was greatly affected by slope and altitude. Changes in FPAR were fastest at slopes of 15~35 degrees and highest at altitude of 700~2 100 m. The effect of slope direction on FPAR was limited. There was little difference in FPAR among different slope directions except for the south where the FPAR was relatively lower. (3)The FPAR data from 1982 to 2015 demonstrated seasonal variation. The inter-annual variation in FPAR was most significant in winter, in which FPAR in about 78.5% of the area increased. FPAR declined most significantly in the fall. (4) FPAR derived from both the MODIS NDVI and GIMMS NDVI data demonstrated a small, temporary decline in 2012. The trend of inter-annual variation in FPAR was largely different among different vegetation types. In conclusion, the FPAR data from 1982 to 2015 in the Tibetan plateau demonstrated both spatial and seasonal variation, which may have important implications for further studies concerning climate and environmental changes in the region.     

Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery

The fraction of photosynthetically active radiation (FPAR) absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index (NDVI) for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels (～70% crop cover), is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.     

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。     

多云雾地区高时空分辨率植被覆盖度构建方法研究

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.     

Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage

It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type,and vice versa;(2)wheat NDVI of farmland vary greatly with different soil background types,and the difference decrease while the vegetation coverage exceed 25%;(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage,NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.     

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

This study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.