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_(GPP)31天,植被指数EOS滞后EOS_(GPP)10—17天。虽然基于3种遥感指数估算的春季和秋季物候都滞后于通量GPP的物候期,但是卫星SIF的物候信息能够更好地捕捉常绿针叶林的生长阶段。同时,春季温度是影响森林生长季开始时间的最重要因素;秋季水分和辐射是影响生长季结束时间的关键因素。由此可见,SIF估算的亚热带常绿针叶林的春季和秋季物候的滞后时间要短于传统植被指数,能更好地追踪常绿林光合作用的季节性,为深入研究陆地生态系统碳循环及其对气候变化的响应提供重要的基础。     

高光谱与拟合多光谱植被指数反演武夷山亚高山草甸LAI的对比研究

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, R2 = 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, R2 = 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, R2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。     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

乌梁素海湿地芦苇最大羧化速率的高光谱遥感

湿地植被生产力和固碳潜力的研究是全球碳循环和全球变化的热点研究问题。湿地植被的光合能力能够指示其生长的健康状态。最大羧化速率是重要的植被光合参数之一,对精确模拟湿地植被光合作用和气体交换模型中的固碳过程具有重要的作用。以内蒙古乌梁素海湖泊湿地为研究区,进行了芦苇叶片光合参数和光谱的测量。芦苇叶片最大羧化速率(V_(cmax))数值是基于Farquhar光合作用模型,从光合测量获取的A-C_i曲线计算并校正到25℃得到的。分别基于bootstrap PLSR模型、单波段和高光谱植被指数(包括简单比值指数SR和归一化差值指数ND),构建湿地芦苇叶片最大羧化速率(V_(cmax))估算模型。基于高光谱遥感图像HJ-1A HSI,采用ND高光谱指数中具有较高V_(cmax)估算精度的入选波段702和756 nm,获取研究区湿地芦苇最大羧化速率空间分布图。研究结果表明,湿地植被光谱特征和高光谱植被指数,可用于估算湿地芦苇V_(cmax),其中最高精度产生于基于bootstrap PLSR模型的建模方法(R~2=0.87,RMSECV=3.90,RPD=2.72),ND高光谱指数的V_(cmax)估算精度高于SR高光谱指数的估算精度;从获取的V_(cmax)空间分布图上提取估算值,其与测量值对比,存在较好的相关性(R~2=0.80,RMSE=4.74)。     

华南地区亚热带树木叶面积指数的高光谱反演研究

为构建树种叶面积指数的估算模型,以NDVI、RVI、FREP、CIGreen、CIRed-edge、MSAVI2为高光谱特征变量,通过统计分析,确定反演树种叶面积指数的最佳光谱特征变量,构建华南农业大学校园内50种亚热带树木的叶片反射率和叶面积指数(LAI)模型。结果表明,6种高光谱特征变量与树种叶面积指数间都具有极显著相关性,其中红边位置反射率(FREP)和比值植被指数(RVI)与LAI的拟合方程的R2都大于0.8,决定系数分别为0.820和0.811。经过精度验证,FREP估算的均方根误差(RMSE)只有0.13,该回归模型为估测亚热带典型树种的叶片LAI最佳模型。从高光谱遥感的角度结合亚热带植被的群落结构特点来看,建立的红边位置光谱反射率与叶面积指数的回归模型普遍具有较高的拟合度,所以利用高光谱特征变量反演亚热带树木叶片的叶面积指数等植被参数的应用前景较好。     

基于MODIS数据的神农架大九湖泥炭藓沼泽植被指数变化研究

泥炭藓是陆地生态系统中最重要的固碳植物之一,固碳量约占全球土壤碳的15%。近几十年来,由全球气候变暖导致的泥炭藓沼泽水热状况变化对泥炭藓的固碳量和速率产生影响。选取我国最重要的亚高山泥炭沼泽——神农架大九湖泥炭藓沼泽为试验区,以分析中纬度地区泥炭藓沼泽植被生长状况受气候变化的影响。研究以2000—2017年MODIS植被指数NDVI和EVI为数据源,通过对比Logistic模型订正后的NDVI和EVI时间序列在泥炭藓沼泽植被生长状况监测中的优劣,选出最佳指标以获得18年来泥炭藓沼泽植被生长状况的变化趋势。研究结果表明:1)Logistic模型能够很好的消除泥炭藓沼泽植被指数时间序列的噪声;2)在季节和年际两个时间尺度上,EVI对泥炭藓沼泽植被生长状况的监测效果均优于NDVI。在季节周期上,虽然EVI和NDVI均得到泥炭藓沼泽植被生长周期规律,但EVI更灵敏。在年际分析中,EVI有更大的值域响应空间,以准确反映泥炭藓沼泽植被的年际变化规律;3)由EVI获得18年来泥炭藓沼泽植被变化趋势指出,泥炭藓沼泽植被呈显著微弱增长,年均EVI增长率为3.8‰(R~2=0.45,P0.01)。相比于EVI年均值,EVI年内最大值(R~2=0.47,P0.01)更敏锐的反映泥炭藓沼泽植被生长状况的动态变化。     

基于FLUXNET观测数据与VPM模型的森林生态系统光合作用关键参数优化及验证

生态系统总初级生产力(GPP)是全球生态系统碳循环研究的重要组成部分.植被最大光能利用率(ε_max)是陆地生态系统GPP模拟的关键参数.本文基于植被光合模型(VPM)和全球通量网(FLUXNET)40个站点(179条站点年数据)的涡度相关通量观测数据,采用单因素轮换法对VPM模型进行参数敏感性分析,并利用交叉验证法对全球森林生态系统的光合作用关键参数进行优化和验证.结果表明:森林生态系统GPP模型受ε_max、光合最高温度(T_max)以及光合最适温度(T_opt)的影响最大;优化后的ε_max在不同植被类型之间存在明显差异,介于0.05～0.08 μmol CO₂·μmol^-1 PAR,常绿阔叶林>常绿针叶林>混交林>落叶阔叶林;优化后的森林生态系统T_max为38～48 ℃,T_opt为18～22 ℃;利用分植被类型优化后的模型关键参数,VPM模型可较好地模拟全球主要森林生态系统GPP的季节和年际变化.     

基于集成生物圈模型的南岭不同植被类型碳收支研究

广东南岭保存着世界上同纬度带上最完整的亚热带植被,森林资源丰富,具有巨大的固碳潜力。然而,目前该地区不同森林植被类型的碳收支年积累量特征及月动态规律尚不明确。选择广东南岭国家级自然保护区内沟谷常绿阔叶林、山地常绿阔叶林、针阔叶混交林和山顶常绿阔叶矮林4种典型森林植被为研究对象,运用集成生物圈模型(IBIS)对其2020年总初级生产力(GPP)、净初级生产力(NPP)、净生态系统生产力(NEP)和土壤异养呼吸(R_h)进行模拟,利用样地调查数据对NPP模拟结果进行验证,分析该地区不同植被类型的碳收支年积累量特征及月变化特征。研究结果表明,2020年南岭不同植被类型GPP、NPP、NEP和R_h的平均值分别为1.709、0.718、0.596和0.123 kg C m^-2 a^-1,4种植被类型中GPP最高的是沟谷常绿阔叶林,NPP、NEP最高的是山地常绿阔叶林,山顶常绿阔叶矮林的GPP、NPP和NEP均相对较低。南岭不同植被类型全年各月均表现出碳汇(NEP>0),逐月NPP和NEP均表现为双峰变化规律...     

基于小波变换的毛竹叶片净光合速率高光谱遥感反演

在对毛竹林叶片高光谱反射率数据进行小波变换的基础上,寻找和确定最佳的小波植被指数反演毛竹林叶片的净光合速率(P_n).结果表明:理想的高频小波植被指数反演得到的P_n精度高于低频小波植被指数和光谱植被指数,其中,由小波分解第一层高频系数构建的归一化植被指数、比值植被指数和差值植被指数与P_n之间的相关性最好,R~2为0.7,均方根误差(RMSE)较低,为0.33;而低频小波植被指数反演P_n的精度低于光谱植被指数.由各层理想小波植被指数所构建的多元线性模型反演得到毛竹叶片P_n与实测P_n之间具有显著的相关关系,R~2为0.77,RMSE为0.29,且精度明显高于基于光谱植被指数所构建的多元线性模型.与光谱植被指数反演毛竹P_n的敏感波段仅局限于可见光波段相比,小波植被指数探测的敏感波长范围更广,包含了可见光及多个红外波段.高光谱数据在经过小波变换后能够发现更多反映毛竹P_n的细节信息,且整体反演精度比原始光谱有了显著提高,研究结果为基于高光谱遥感反演植被P_n提供了一种新的可选方法.     

落叶阔叶林冠层光合有效辐射分量的遥感模拟与分析

冠层绿色叶片(光合组分)的光合有效辐射分量(绿色FPAR)真实地反映了植被与外界进行物质和能量交换的能力,获取冠层光合组分吸收的太阳光合有效辐射,对生态系统生产力的遥感估算精度的提高具有重要的意义。研究以落叶阔叶林为例,基于SAIL模型模拟森林冠层光合组分和非光合组分吸收的光合有效辐射,研究冠层FPAR变化规律以及与植被指数的相关关系。结果表明,冠层结构的改变会影响冠层对PAR的吸收能力,冠层绿色FPAR的大小与植被面积指数及光合组分面积比相关;在高覆盖度植被区,冠层绿色FPAR占冠层总FPAR的80%以上,非光合组分的贡献较小,但在低植被覆盖区,当光合组分和非光合组分面积相同时,绿色FPAR不及冠层总FPAR的50%;相比于NDVI,北方落叶阔叶林冠层EVI与绿色FPAR存在更为显著的线性相关关系(R~20.99)。     

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R² = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower‐based measurement of SIF and leaf‐level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R² = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf F_q’/F_m’, the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R² = 0.79; P < 0.0001). We also found that canopy SIF and SIF‐derived GPP (GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopy GPP_SIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopy GPP_SIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopy GPP_SIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.     

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.     

Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China

We estimated the light use efficiency (LUE ) via vegetation canopy chlorophyll content (CCC _canopy) based on in situ measurements of spectral reflectance, biophysical characteristics, ecosystem CO ₂ fluxes and micrometeorological factors over a maize canopy in Northeast China. The results showed that among the common chlorophyll‐related vegetation indices (VI s), CCC _canopy had the most obviously exponential relationships with the red edge position (REP ) (R ² = .97, p  <  .001) and normalized difference vegetation index (NDVI ) (R ² = .91, p  <  .001). In a comparison of the indicating performances of NDVI , ratio vegetation index (RVI ), wide dynamic range vegetation index (WDRVI ), and 2‐band enhanced vegetation index (EVI 2) when estimating CCC _canopy using all of the possible combinations of two separate wavelengths in the range 400?1300 nm, EVI 2 [1214, 1259] and EVI 2 [726, 1248] were better indicators, with R ² values of .92 and .90 (p  <  .001). Remotely monitoring LUE through estimating CCC _canopy derived from field spectrometry data provided accurate prediction of midday gross primary productivity (GPP ) in a rainfed maize agro‐ecosystem (R ² = .95, p  <  .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties.     

Diverse Optical and Photosynthetic Properties in a Neotropical Dry Forest during the Dry Season: Implications for Remote Estimation of Photosynthesis1

Using optical and photosynthetic assays from a canopy access crane, we examined the photosynthetic performance of tropical dry forest canopies during the dry season in Parque Metropolitano, Panama City, Panama. Photosynthetic gas exchange, chlorophyll fluorescence, and three indices derived from spectral reflectance (the normalized difference vegetation index, the simple ratio, and the photochemical reflectance index) were used as indicators of structural and physiological components of photosynthetic activity. Considerable interspecific variation was evident in structural and physiological behavior in this forest stand, which included varying degrees of foliage loss, altered leaf orientation, stomatal closure, and photosystem II downregulation. The normalized difference vegetation index and the simple ratio were closely related to canopy structure and absorbed radiation for most species, but failed to capture the widely divergent photosynthetic behavior among evergreen species exhibiting various degrees of downregulation. The photochemical reflectance index and chlorophyll fluorescence were related indicators of photosynthetic downregulation, which was not detectable with the normalized difference vegetation index or simple ratio. These results suggest that remote sensing methods that ignore downregulation cannot capture within‐stand variability in actual carbon flux for this diverse forest type. Instead, these findings support a sampling approach that derives photosynthetic fluxes from a consideration of both canopy light absorption (e.g., normalized difference vegetation index) and photosynthetic light‐use efficiency (e.g., photochemical reflectance index). Such sampling should improve our understanding of controls on photosynthetic carbon uptake in diverse tropical forest stands.     

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.     

Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

In this study we examined ecosystem respiration (R_ECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of R_ECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of R_ECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of R_ECO. The maximum seasonal leaf area index (LAI_MAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature T_ref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r²=0.52, P<0.001, n=104) even within each PFT. Besides LAI_MAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (N_depo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAI_MAX) which performed well in predicting the spatio‐temporal variability of R_ECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands.     

An improved approach for remotely sensing water stress impacts on forest C uptake

Given that forests represent the primary terrestrial sink for atmospheric CO₂, projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production (GPP) responses to water stress are commonly based on remotely sensed changes in canopy ‘greenness’ (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress (SSWS) – defined here as drought‐induced decline in GPP without a change in greenness. Current satellite‐derived estimates of GPP use a vapor pressure deficit (VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite‐derived estimates of GPP with eddy‐covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark (MO) and Morgan Monroe State Forest (MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO (r² = 0.09) and MMSF (r² = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO (r² = 0.83) and for a severe drought year at the MMSF (r² = 0.82). Collectively, our results suggest that large‐scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought.     

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.     

Future atmospheric CO2 leads to delayed autumnal senescence

Growing seasons are getting longer, a phenomenon partially explained by increasing global temperatures. Recent reports suggest that a strong correlation exists between warming and advances in spring phenology but that a weaker correlation is evident between warming and autumnal events implying that other factors may be influencing the timing of autumnal phenology. Using freely rooted, field‐grown Populus in two Free Air CO₂ Enrichment Experiments (AspenFACE and PopFACE), we present evidence from two continents and over 2 years that increasing atmospheric CO₂ acts directly to delay autumnal leaf coloration and leaf fall. In an atmosphere enriched in CO₂ (by ～45% of the current atmospheric concentration to 550 ppm) the end of season decline in canopy normalized difference vegetation index (NDVI) – a commonly used global index for vegetation greenness – was significantly delayed, indicating a greener autumnal canopy, relative to that in ambient CO₂. This was supported by a significant delay in the decline of autumnal canopy leaf area index in elevated as compared with ambient CO₂, and a significantly smaller decline in end of season leaf chlorophyll content. Leaf level photosynthetic activity and carbon uptake in elevated CO₂ during the senescence period was also enhanced compared with ambient CO₂. The findings reveal a direct effect of rising atmospheric CO₂, independent of temperature in delaying autumnal senescence for Populus, an important deciduous forest tree with implications for forest productivity and adaptation to a future high CO₂ world.