Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool-temperate deciduous broadleaf forest in Takayama, Japan

Revealing the seasonal and interannual variations in forest canopy photosynthesis is a critical issue in understanding the ecological mechanisms underlying the dynamics of carbon dioxide exchange between the atmosphere and deciduous forests. This study examined the effects of temporal variations of canopy leaf area index (LAI) and leaf photosynthetic capacity [the maximum velocity of carboxylation (V _cmax)] on gross primary production (GPP) of a cool-temperate deciduous broadleaf forest for 5 years in Takayama AsiaFlux site, central Japan. We made two estimations to examine the effects of canopy properties on GPP; one is to incorporate the in situ observation of V _cmax and LAI throughout the growing season, and another considers seasonality of LAI but constantly high V _cmax. The simulations indicated that variation in V _cmax and LAI, especially in the leaf expansion period, had remarkable effects on GPP, and if V _cmax was assumed constant GPP will be overestimated by 15%. Monthly examination of air temperature, radiation, LAI and GPP suggested that spring temperature could affect canopy phenology, and also that GPP in summer was determined mainly by incoming radiation. However, the consequences among these factors responsible for interannual changes of GPP are not straightforward since leaf expansion and senescence patterns and summer meteorological conditions influence GPP independently. This simulation based on in situ ecophysiological research suggests the importance of intensive consideration and understanding of the phenology of leaf photosynthetic capacity and LAI to analyze and predict carbon fixation in forest ecosystems.     

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.     

表征亚热带常绿林光合作用季节变化特征的多种植被指数

利用遥感方法可以在区域尺度反演地表植被的光合生理状况和生产力变化,但亚热带常绿林冠层结构季节变化较小,传统的光谱植被指数对植被光合作用难以准确捕捉。利用2014—2015年中国科学院广东省鼎湖山森林生态试验站多角度自动光谱观测系统的光谱反射数据,分别反演传统冠层结构型植被指数(NDVI)、光合生理生化型植被指数(CCI)和叶绿素荧光型植被指数(NDFI_(685)和NDFI_(760)),并利用不同类型植被指数的组合,构建多元线性回归模型。结果表明:亚热带常绿针阔混交林三种类型植被指数均与GPP的动态变化有显著的相关性,其中,NDVI是表征GPP较优的植被指数(R~2=0.60,P0.01),其次为CCI(R~2=0.55,P0.01),而NDFI能够作为辅助指数,有效提高NDVI(R~2=0.68,P0.001)和CCI(R~2=0.67,P0.001)表征GPP的程度。多个植被指数参与构建的多元回归模型能够有效提高亚热带地区常绿林GPP季节动态变化的拟合精度,提升遥感精确评估亚热带森林生产力的能力。     

Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

Aims Accurate remote estimation of the fraction of absorbed photosynthetically active radiation (fAPAR) is essential for the light use efficiency (LUE) models. Currently, one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index (NDVI). Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method. This study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods To investigate the method of remote estimation of fAPAR seasonal dynamics, the CASA (Carnegie–Ames–Stanford Approach) model and spectral vegetation indices (VIs) were used for in situ measurements of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important findings The results showed that the fAPAR increased rapidly with the day of year during the vegetative stage, it remained relatively stable at the stage of reproduction, and finally decreased slowly during the senescence stage. In addition, fAPAR green [fAPAR green = fAPAR × (green LAI/green LAI max)] showed clearer seasonal trends than fAPAR. The NDVI, red-edge NDVI, wide dynamic range vegetation index, red-edge position (REP) and REP with Sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season. In a comparison of the predictive performance of VIs for the whole growing season, REP was the most appropriate spectral index, and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.     

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)。     

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.     

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.     

祁连山区青海云杉林冠层叶面积指数的反演方法

叶面积指数(Leaf area index, LAI)是陆地生态系统的一个十分重要的结构参数。随着空间精细化模型的发展和基于过程的分布式模拟技术的应用, 对LAI的区域估算显得越来越重要, 但目前尚缺乏有效的估算手段。该项研究以青海云杉(Picea crassifolia)林为研究对象, 利用LAI-2000冠层分析仪、鱼眼镜头法和经验公式法对林冠层LAI进行了测定, 观测值分别为1.03~3.70、0.48~2.26和2.27~8.20, 显然, 仪器测定值偏低。针对针叶的集聚效应导致仪器测定值偏低的现象, 利用跟踪辐射与冠层结构测量仪(TRAC)测定的青海云杉林聚集系数计算调整系数, 对鱼眼镜头法获取的LAI值进行订正。根据高分辨率的遥感数据反演青海云杉林的植被指数与LAI的关系, 最后获得了较合理的该地区林冠层LAI的空间分布图。     

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.     

Effects of Season and Successional Stage on Leaf Area Index and Spectral Vegetation Indices in Three Mesoamerican Tropical Dry Forests1

We compared plant area index (PAI) and canopy openness for different successional stages in three tropical dry forest sites: Chamela, Mexico; Santa Rosa, Costa Rica; and Palo Verde, Costa Rica, in the wet and dry seasons. We also compared leaf area index (LAI) for the Costa Rican sites during the wet and dry seasons. In addition, we examined differences in canopy structure to ascertain the most influential factors on PAI/LAI. Subsequently, we explored relationships between spectral vegetation indices derived from Landsat 7 ETM+ satellite imagery and PAI/LAI to create maps of PAI/LAI for the wet season for the three sites. Specific forest structure characteristics with the greatest influence on PAI/LAI varied among the sites and were linked to climatic differences. The differences in PAI/LAI and canopy openness among the sites were explained by both the past land‐use history and forest management practices. For all sites, the best‐fit regression model between the spectral vegetation indices and PAI/LAI was a Lorentzian Cumulative Function. Overall, this study aimed to further research linkages between PAI/LAI and remotely sensed data while exploring unique challenges posed by this ecosystem.     

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。     

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。     

岷江上游植被冠层降水截留的空间模拟

 通过对岷江上游实地踏查和定位观测研究,结合MODIS遥感数据,利用“3S”技术对岷江上游植被冠层降水截留进行了空间模拟。研究结果表明：岷江上游植被叶面积指数（LAI）与增强性植被指数（EVI）以二项式关系拟合效果较好。由于归一化植被指数（NDVI）存在的饱和问题,研究采用EVI反演LAI,统计结果表明：岷江上游LAI值在0～2之间的占28.57%,在2～4.5之间的占63.06%,大于4.5的占8.37%,其中LAI最大值为7.394;从冠层最大降水截留模拟结果来看: 植被较好的地区,如卧龙、米亚罗的植被冠层最大降水截留量较大,而干旱河谷、上游高山草甸等地的植被冠层最大降水截留量相对较低;附加冠层降水截留与降雨量呈线性相关,模型验证时以此为基础,模型模拟的结果较为理想。     

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...     

Modeling the effects of Hurricane Hugo on spatial and temporal variation in primary productivity and soil carbon and nitrogen in the Luquillo Experimental Forest, Puerto Rico

Hurricanes account for much of the spatial and temporal variation in forest productivity and soil organic matter pools in many forest ecosystems. In this study, we used an ecosystem level model, TOPOECO, to simulate the effects of Hurricane Hugo (18 September 1989) on spatial and temporal patterns of gross primary productivity (GPP), net primary productivity (NPP), soil organic carbon (SOC) and nitrogen over the entire Luquillo Experimental Forest (LEF), Puerto Rico, a tropical rainforest. Our simulation results indicated that simulated annual GPP increased by an average of 30% five years after Hugo in the Tabonuco forest at low elevations where there was a fast recovery of the canopy, whereas simulated GPP decreased by an average of 20% in the Palm and Dwarf forests at high elevations as a result of the slow recovery of the canopy. Simulated annual NPP in the Palm and Dwarf forests also did not recover to pre-Hugo levels within 5 years. Simulated storages of SOC, CO₂ emission from decomposition of SOC and total soil nitrogen increased slightly but N mineralization rate increased significantly in all four vegetation types due to the massive input of plant materials from Hugo at low elevations and the slow decomposition at high elevations.     

Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area

Frequent and continuous time series is required for the detection of plant phenology and vegetation succession. The launch of novel remote sensor MODIS (moderate resolution imaging spectroradiometer) provided us with an opportunity to make a new trial of studying the rapid vegetation succession in estuarine wetlands. In this study, the spatiotemporal variations of vegetation cover and tidal flat elevation along a transect (covering 6 pixels of MODIS) of an estuarine wetland at Dongtan, Chongming Island, in Yangtze River estuary, China were investigated to assess its rapid vegetation succession and physical conditions. By combining the field data collected, the time series of MODIS-based VIs (vegetation indices), including NDVI (normalized difference vegetation index), EVI (enhanced vegetation index) and MSAVI (modified soil adjusted vegetation index), and a water index, LSWI (land surface water index) were utilized to characterize the rapid vegetation succession between 2001 and 2006. We found that NDVI, EVI and MSAVI exhibited significant spatial and temporal correlations with vegetation succession, while LSWI behaved in a positive manner with surface water and soil moisture along with the successional stages. In order to take the advantages of both VIs and water index, a composite index of VWR (vegetation water ratio) combining LSWI and EVI or MSAVI was proposed in this paper. This index facilitates the identification of vegetation succession by simply comparing the values of VWR at different stages, and therefore it could track vegetation succession and estimate community spread rate. Additionally, this study presented an attempt of using MODIS datasets to monitor the change of tidal flat elevation, which demonstrated a potential remote sensing application in geodesy of coastal and estuarine areas.     

青海湖北岸高寒草甸草原生态系统CO2通量特征及其驱动因子

 草甸草原是青藏高原的重要植被类型, 与其他植被类型相比, 其碳交换过程和驱动机理的研究仍较薄弱。利用青海湖东北岸草甸草原的涡度相关系统观测的连续数据(2010年7月1日–2011年6月30日), 分析了草甸草原CO₂通量特征及其驱动因子。结果表明: 草甸草原净生态系统CO₂交换量(NEE)在植物生长季的5–9月, 其日变化主要受控于光合光量子通量密度(PPFD); 而非生长季(10月21日–4月19日)和生长季初(4月下旬)、末期(10月中上旬) NEE的日变化主要受气温(T_a)的影响。CO₂
日最大吸收值和释放值分别出现在7月1日(11.37 g CO₂·m^–2·d^–1)和10月21日(4.04 g CO₂·m^–2·d^–1)。逐日NEE主要受控于T_a, 两者关系可用指数线性(explinear)方程表示(R² = 0.54, p < 0.01)。叶面积指数(LAI)和增强型植被指数(EVI)对逐日NEE的影响表现为渐近饱和型, LAI和T_a交互作用明显(p < 0.05), EVI的主效应强烈(p < 0.001)。生态系统的呼吸熵(Q₁₀)为2.42, 总呼吸(R_eco)约占总初级生产力(GPP)的74%。生长季适度的昼夜温差(<14.8 ℃)有利于系统的碳蓄积。研究时段该草甸草原作为碳汇从大气吸收271.31 g CO₂· m^–2。     

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.     

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。