Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at 'Takayama' super site in central Japan.Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × V cmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPP max) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance.Important findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPP max relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPP max over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.     

不同干旱条件下夏玉米全生育期冠层吸收光合有效辐射比的高光谱遥感反演

冠层吸收光合有效辐射比(fAPAR)是植被生产力遥感模型的重要参数.但关于不同干旱条件下作物全生育期的fAPAR遥感反演研究仍未见报道.本研究利用2015年夏玉米5个灌水处理模拟试验的高光谱反射率和fAPAR观测资料,分析了不同干旱条件下夏玉米关键生育期fAPAR和高光谱反射率变化特征,探讨了fAPAR与反射率、一阶导数光谱反射率和植被指数的关系.结果表明: 轻度水分胁迫和充分供水条件下,fAPAR较高;重度水分胁迫和重度持续干旱条件下,fAPAR较低.冠层可见光、近红外光和短波红外光区的反射率与fAPAR分别呈负相关、正相关和负相关关系.fAPAR与可见光和短波红外光区的383、680和1980 nm附近的反射率的相关性最强,相关系数均达-0.87.一阶导数光谱反射率与fAPAR相关性强且稳定的波段为580、720和1546 nm,相关系数分别为-0.91、0.89和0.88. 9个常用植被指数与fAPAR呈线性或对数关系,其中,增强型植被指数、复归一化植被指数、土壤调节植被指数和修正的土壤调节植被指数与fAPAR的关系模型最好,决定系数(R²)均在0.88以上,平均相对误差分别为16.6%、16.6%、16.7%和16.2%;基于一阶导数光谱反射率与fAPAR的对数关系在(720±5) nm波段处的模拟效果较好,R²达0.86;直接选择反射率数据估算fAPAR的效果较差,R²最高为0.81.研究结果可为fAPAR的准确反演及评估作物干旱状况提供支撑.     

MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究

利用光谱分辨率为3nm的ASD FieldSpec UV／VNIR光谱仪获得了2002和2003年水稻整个生长期的高光谱数据,同时对水稻叶面积指数(LAI)和叶绿素含量(CHL．Ｃ)进行了测定,对中分辨率成像光谱仪(MODIS)的增强植被指数(EVI)、归一化植被指数(NＤVI)以及红边位置(REP)与LAI及CHL．C之间的关系进行了研究．结果表明。LAI与冠层光谱在可见光、近红外波段相关性较好,叶绿素含量与冠层光谱在红光波段相关性较好．EVI、REP和LAI之间的相关关系不受水稻覆盖率的影响;NDVI与LAI的相关关系在水稻低覆盖率情况下较好;在水稻高覆盖率情况下。EVI和REP比NDVI与LAI之间的相关关系要好．MODIS-NDVI、EVI及REP与叶片叶绿素含量相关性较好．由此可见,EVI和REP可以有效地监测水稻LAI和CHL．C．     

不同大气校正方法对森林叶面积指数遥感估算影响的比较

利用TM原始图像以及经过6S模型和基于影像自身的Gilabert模型大气校正后的地面绝对反射率图像,分别计算了褒河流域阔叶林和针阔混交林2种林型的5类光谱植被指数(SR、NDVI、MNDVI、ARVI和RSR),并建立各林型森林叶面积指数与同时相的各个植被指数的相关关系。结果表明,2种大气校正模型均显著提高了各植被指数与森林叶面积指数的相关关系,除了对森林叶面积指数与植被指数SR和NDVI的相关关系影响不显著外,对森林叶面积指数与植被指数MNDVI、ARVI和RSR相关关系的影响均非常显著。说明不同大气校正模型对叶面积指数的遥感估算结果有较大影响。因此,在利用遥感数据进行定量分析、信息提取和生态遥感应用时,不仅要进行大气校正,而且还要慎重选择大气校正模型和植被指数。     

Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing

Aims Determination of canopy photosynthetic parameters is key to accurate simulation of ecosystem function by using remote sensing methods. Currently, remote estimation of vegetation canopy structure characteristics has been widely adopted. However, directly estimating photosynthetic variables (photosynthetic capacity and efficiency) at canopy scale based on field spectrometry combined with CO₂ flux measurements is rare.
Methods In this study, we remotely estimated solar radiation use efficiency (εN, net ecosystem CO₂ exchange/absorbed photosynthetically active radiation (NEE_CO2/APAR); εG, gross primary productivity/absorbed photosynthetically active radiation (GPP/APAR); α, apparent quantum efficiency) and photosynthetic capacity (P_max) based on in situ measurements of spectral reflectance and ecosystem CO₂ fluxes, along with observational data on micrometeorological factors during the entire growing season for a maize canopy in Northeast China.
Important findings Results showed that the seasonal variations in P_max and α exhibited a single peak; whereas the values of εN and εG were higher at the start of vegetative stage and then rapidly decreased with the development of maize until displaying a single peak at the intermediate and late stages of the growing season, coinciding with the occurrence of peak values in P_max. A comparison was made on the predictive performance based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), 2-band enhanced vegetation index (EVI2), and chlorophyll index (CI) in estimating four canopy photosynthetic parameters with any combination of two separate wavelengths at the range of 400–1 300 nm, which showed that EVI2 was most closely and linearly related to photosynthetic capacity and efficiency. This study demonstrates that multi-spectral remote sensing information is sensitive to the variations in canopy photosynthetic parameters in maize field and can be used to quantitatively monitor seasonal dynamics of canopy photosynthesis, and to accurately assess crop productivity and ecosystem CO₂ exchange capacity.     

基于MOD09产品的水稻叶面积指数和叶绿素含量的遥感估算模型

基于中分辨率成像光谱仪MODIS (moderate resolution imaging spectroradiometer)反射率产品MOD09的同步野外实测水稻叶面积指数（LAI）和叶绿素含量（Chltot）相关数据,探寻用MOD09产品提取的植被指数(VIs)与水稻LAI和Chltot之间的相关性以及估算模型. 结果表明,MOD09计算的VI数值比MODIS前3个波段数值偏大,归一化植被指数NDVI (normalized difference vegetation index) 值普遍比增强性植被指数EVI(enhanced vegetation index) 值大. 通过4种不同植被指数与LAI相关性的比较,得出EVI与LAI的相关关系在水稻各个生育期优于其它植被指数,基于MOD09-EVI建立水稻LAI的遥感估算模型,经实际地面同步数据检验, 模型精度较高. 因而, MOD09-EVI较适用于水稻叶面积指数的实时遥感监测. MOD09红波段与Chltot之间的相关性在水稻前中期达到显著,并且优于其它植被指数,基于MOD09红波段建立了水稻前中期Chltot的估算模型并进行了精度检验. 除水稻孕穗期叶绿素含量估算模型的相关系数和F值通过了显著性检验外, 其余生育期估算模型都没有通过显著性检验.     

基于多角度植被指数的马尾松林LAI反演方法

CHRIS/PROBA是目前具有最高空间分辨率（17 m×17 m）的星载多角度高光谱数据,该款数据在反演植被垂直结构参数,如树高、叶面积指数（leaf area index,LAI）等方面具有重要的应用前景。基于四尺度几何光学模型得到马尾松（Pinus massoniana Lamb.）冠层的归一化差分植被指数（normalized difference vegetation index,NDVI）各向异性分布规律,利用CHRIS红光特征波段和近红外特征波段构建一种新型多角度植被指数（normalized hotspot-dark-spot difference vegetation index,NHDVI）,并将其应用于CHRIS数据对马尾松林的LAI遥感估算上。结果显示：（1）相比归一化差分植被指数（NDVI）与土壤调节植被指数（soil adjusted vegetation index,SAVI）而言,NHDVI能很好地融合光谱信息与角度信息,与地面实测LAI的决定系数达到0.7278;（2）利用NHDVI-LAI统计回归模型方法来反演LAI值,将得到的LAI值与地面实测值进行相关性分析,结果拟合优度达到0.8272,均方根误差RMSE为0.1232。与传统植被指数相比,包含角度信息的多角度植被指数对LAI的反演在精度上有较大提升,同时比基于辐射传输模型的反演方法更简易、实用。     

烟草叶面积指数的高光谱估算模型

叶面积指数(1eaf area index,LAI)是重要的生物物理参数,亦是各种生态模型、生产力模型以及碳循环研究等的重要生物物理参量,因此具有重要的研究意义。为了探索不同高光谱模型监测烟草叶面积指数LAI的精度,在烟草伸根期,旺长期和成熟期采用ASD Fieldspec HH光谱仪测定了不同水氮条件下烟草冠层的高光谱反射率和叶面积指数数据。选用四个常用的植被指数RVI (ratio vegetation index)、NDVI (normalized difference vegetation index)、MTVI2（Modified second triangular vegetation index）、MSAVI（Modified Soil-adjusted vegetation index）和PCA (principal component analysis)、neural network (NN)三种方法对烟草LAI进行了估算,比较分析了三种方法的估算结果。研究结果表明,植被指数法,主成分分析,神经网络方法LAI都取得了较为理想的结果,其中植被指数法可以较为精确反演烟草LAI,验证模型确定性系数在0.76～0.85之间,主成分分析方法和神经网络方法精度较高,分别为0.938和0.889。主成分分析方法验证模型的稳定性更好,其验证模型的RMSE为0.172,低于四个植被指数和神经网络。MTVI2和MSAVI能较好地去除土壤、大气等条件影响,反演精度高于RVI和NDVI。与基于植被指数建立的模型相比,主成分分析和神经网络可以更好的提高LAI的反演精度。     

Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

Aims

Our aims were to identify the primary factors involved in soil respiration (R_s) variability and the role that spectral vegetation indices played in R_s estimation in irrigated and rainfed agroecosystems during the growing season.

Methods

We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CI_{green edge}) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for R_s estimation. Different statistical models were used to analyze the dependencies of R_s on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems.

Results

The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in R_s. Residual analysis indicated that R_s was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of R_s should be included in the R_s model to describe the growing-season dynamics of R_s. Among the three VIs, CI_{green edge} showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CI_{green edge} into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of R_s.

Conclusions

Our results suggest that spectral vegetation index from remote sensing could be used to estimate R_s, which will be helpful for the development of a future R_s model over a large spatial scale.     

平稳小波变换在冬小麦SPAD高光谱监测中的应用

在2010与2011年度冬小麦生长季通过大田小区试验,利用ASD便携式野外光谱仪和SPAD 502叶绿素计实测冬小麦冠层的高光谱反射率与SPAD值.分析不同SPAD值下的冬小麦冠层光谱特征,建立了基于归一化植被指数（NDVI）与比值植被指数（RVI）、小波能量系数的不同生育期冬小麦SPAD估算模型.结果表明： 随着SPAD值的增大,“绿峰”与“红谷”特征愈加明显.在冬小麦返青期、拔节期、抽穗期、灌浆期NDVI估算SPAD的效果较好,估算模型的R²分别为0.7957、0.8096、0.7557、0.5033.小波能量系数回归模型可以提高冬小麦SPAD的估算精度,在返青期、拔节期、抽穗期、灌浆期以高频、低频小波能量系数为自变量的冬小麦SPAD估算模型的R²分别达到0.9168、0.9154、0.8802、0.9087.     

光谱植被指数与水稻叶面积指数相关性的研究

 综合分析比较了几种常见光谱植被指数与水稻（Oryza sativa）叶面积指数的相关性及其预测力。结果表明,植被指数的预测力在水稻营养生长旺盛期间最好。植被指数的预测力主要依赖于叶面积指数(LAI)的整体变化范围。因此,综合不同生育时期和氮肥处理的试验资料,光谱植被指数能准确地预测LAI的变化。LAI与各植被指数均呈曲线相关,与比值植被指数(RVI)、再归一化植被指数(RDVI)和R810/R560显著幂相关,与归一化植被指数(NDVI)、垂直植被指数(PVI)、差值植被指数(DVI)、土壤调整植被指数(SAVI)和转换型土壤调整指数(TSAVI)显著指数相关。其中,近红外与绿光波段的比值R810/R560的预测力最佳。用不同移栽秧龄、不同密度、不同水分和氮肥处理的数据对R810/R560的表现进行了检验,结果表明估算精度平均为91.22%,估计的均方差根(RMSE)平均为0.480　5,平均相对误差为-0.013。表明宽波段光谱植被指数可以准确地用来监测水稻叶面积指数。     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

玉米农田冠层光合参数的多光谱遥感反演

冠层光合参数的准确定量遥感反演是生态系统遥感模型的核心与关键。基于2011年玉米(Zea mays)整个生长发育期的冠层光谱反射率、生态系统CO₂通量、微气象因子以及玉米光合生理生态指标的观测数据, 开展了玉米农田生态系统冠层光合能力(P_max, 最大光合速率)与光合效率(ε_N, 净CO₂通量交换/吸收光合有效辐射(NEE_CO2/APAR); ε_G, 总初级生产力/吸收光合有效辐射(GPP/APAR); α, 表观量子效率)参数的多光谱遥感反演能力评估研究。结果表明, P_max和α在整个生长季呈现单峰型变化趋势, 分别于7月底、8月初达到峰值, 而光合效率参数ε_N和ε_G在玉米营养生长早期数值较高, 随着玉米生长发育迅速降低, 而后呈现单峰型的变化趋势, 峰值出现时间基本与P_max最大值发生时间一致。基于两波段任意组合的遥感植被指数NDVI (normalized difference vegetation index)、RVI (ratio vegetation index)、WDRVI (wide dynamic range vegetation index)、EVI2 (2-band enhanced vegetation index)和CI (chlorophyll index)与玉米冠层4个光合参数的统计分析表明, EVI2对冠层光合效率与光合能力参数的反演与表征效果最佳。研究表明, 多光谱遥感信息对玉米生态系统冠层光合参数的变异具有较强的敏感性, 可以用来监测玉米冠层光合作用的季节动态变化以及准确定量评估作物生产力和生态系统CO₂交换能力。     

Greater deciduous shrub abundance extends tundra peak season and increases modeled net CO2 uptake

Satellite studies of the terrestrial Arctic report increased summer greening and longer overall growing and peak seasons since the 1980s, which increases productivity and the period of carbon uptake. These trends are attributed to increasing air temperatures and reduced snow cover duration in spring and fall. Concurrently, deciduous shrubs are becoming increasingly abundant in tundra landscapes, which may also impact canopy phenology and productivity. Our aim was to determine the influence of greater deciduous shrub abundance on tundra canopy phenology and subsequent impacts on net ecosystem carbon exchange (NEE) during the growing and peak seasons in the arctic foothills region of Alaska. We compared deciduous shrub‐dominated and evergreen/graminoid‐dominated community‐level canopy phenology throughout the growing season using the normalized difference vegetation index (NDVI). We used a tundra plant‐community‐specific leaf area index (LAI) model to estimate LAI throughout the green season and a tundra‐specific NEE model to estimate the impact of greater deciduous shrub abundance and associated shifts in both leaf area and canopy phenology on tundra carbon flux. We found that deciduous shrub canopies reached the onset of peak greenness 13 days earlier and the onset of senescence 3 days earlier compared to evergreen/graminoid canopies, resulting in a 10‐day extension of the peak season. The combined effect of the longer peak season and greater leaf area of deciduous shrub canopies almost tripled the modeled net carbon uptake of deciduous shrub communities compared to evergreen/graminoid communities, while the longer peak season alone resulted in 84% greater carbon uptake in deciduous shrub communities. These results suggest that greater deciduous shrub abundance increases carbon uptake not only due to greater leaf area, but also due to an extension of the period of peak greenness, which extends the period of maximum carbon uptake.     

Estimating seasonal aboveground biomass of a riparian pioneer plant community: An exploratory analysis by canopy structural data

The aboveground biomass (AGB) of vegetation is of central importance for ecosystem services by providing a measure of productivity. Models have been developed for estimating AGB via canopy structural variables in both fundamental and applied ecological studies. However, the potential of canopy structural variables for describing AGB dynamics throughout a growing season are still unclear. This study focuses on the AGB seasonal dynamics of a pioneer community, Cynodon dactylon (L.) Pers. (Bermuda grass), in a newly-formed riparian habitat at China’s Three Gorges Reservoir. The objectives are (1) to determine the most important structural variable for estimating AGB at different growing stages during the season, and (2) to develop a model that can estimate AGB at the different growing stages and using multiple structural variables. We sampled the C. dactylon community six times during the growing season from May to September 2016. Six variables were engaged in the analysis, including five canopy structural variables, i.e., canopy height (H), canopy cover (CC), leaf area index (LAI), the volume related variables V_LAI (H × LAI) and V_CC (H × CC), and one seasonal growth effect variable (SV). We conducted univariate linear regression analysis to determine the most important estimator of AGB and the best subset regression analysis were used to develop the AGB estimation model. The detected most important AGB estimator changed with different growing stages throughout a season. Canopy structural characteristics of the community are key factors for determining such changes. Cover was the most important variable for AGB estimation during the early growing season and V_LAI was the most important variable in the mid and end of the growing season. The developed best multivariate models explained an additional 11% in AGB variance on average for the different growing stages compared with the univariate models using the most important estimators. SV was found to be useful in developing an acceptance general AGB estimation model appropriate for the entire growing season. The findings of this study are expected to provide knowledge for guiding sampling work and to assist with modeling AGB and understanding the AGB seasonal dynamics in the future.     

基于小波分析的大豆叶绿素a含量高光谱反演模型

 2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆（Glycine max）冠层高光谱反射率 与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized diffe rence vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反 射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大 豆叶绿素a 与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好 得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSRI等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度（R²>0.75）;小波能量系 数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达 0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明, 小波分析可以对高光谱进 行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。     

林下植被对遥感估算马尾松LAI的影响

叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。     

The ratio of transmitted near-infrared radiation to photosynthetically active radiation (PAR) increases in proportion to the adsorbed PAR in the canopy

The daily total photosynthetically active radiation (400??00?nm, PAR) and near-infrared radiation (700??000?nm, NIR) were measured in the understory beneath the canopy (PARt and NIRt) and above the canopy (PARi and NIRi) of a Japanese cool-temperate deciduous broad-leaved forest during the snow-free period (May to November). The integration of spectral radiation for NIR and that for PAR, and the daily integrations of instantaneous NIR and PAR, reduced the noises from the optical difference in spectrum and from canopy structure heterogeneity, sky condition and solar elevation. PARi/PARt was linearly related to NIRt/PARt (R2?=?0.96). The effect of cloudiness was negligible, because the fluctuation of NIRi/PARi was quite small regardless of season and weather conditions compared with the range of NIRt/PARt in the forest. The ratio of NIRt/PARt beneath the canopy was log-linearly related to the in situ leaf area index (LAI) with a wide range from 0 to 5.25 (R2?=?0.97). We conclude that seasonal changes in fAPAR (=?1???PARt/PARi) and LAI of a canopy can be estimated with high accuracy by transmitted NIRt and PARt beneath the canopy.     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_GPP)为第63天,生长季结束时间(EOS_GPP)为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_GPP31天,植被指数EOS滞后EOS_GPP10—17天。虽然基于3种遥...