叶绿素荧光主动与被动联合观测应用前景

叶绿素荧光是研究植物光合生理机制、量化植被光合作用时空格局以及准确理解气候变化背景下陆地生态系统生产力的关键。然而, 目前对于叶绿素荧光主动与被动联合观测的研究还较少。该文对比了叶绿素荧光主动观测与被动观测的优缺点, 展示了叶片尺度和冠层尺度主动与被动联合观测的仪器设备组成, 探讨了主动与被动联合观测在探索叶绿体尺度-叶片尺度-冠层尺度能量在光合、荧光以及热耗散中的分配, 阐明叶绿素荧光与总初级生产力的关联机制, 验证星基日光诱导叶绿素荧光, 解译叶绿素荧光光谱形状4个方面的应用前景。综上, 叶绿素荧光的主动与被动联合观测对于揭示各尺度上荧光与光合作用之间的关联机制, 改善全球尺度植被生产力模型至关重要。     

土壤-植物-大气连续体水热、CO2通量估算模型研究进展

土壤-植物-大气连续体(SPAC)水热、CO2通量的准确估算对理解陆地和大气的物质和能量交换过程有着重要意义.重点阐述了基于过程的土壤-植物-大气连续体水热、CO2通量模型,综述了统计模型、综合模型及基于遥感的模型的发展过程.其中水热通量统计模型包括基于温度和湿度以及基于温度和辐射的方法;CO2通量统计模型包括基于气候因子或蒸散因子以及基于光能利用率的方法.水热通量过程模型包括大叶、双源、多源和多层的水热传输物理模型;CO2通量过程模型包括叶片尺度及由大叶、双叶和多层方法扩展到冠层尺度的生理生态模型以及光合-蒸腾耦合模型.综合模型包括生物物理模型、生物化学模型和生物地理模型.统计模型形式简单,资料易得,对大范围的水热通量模拟具有指导意义;过程模型准确的揭示了水热和CO2通量传输的物理和生理过程,是大尺度综合模型的基础.未来生态系统水热、CO2通量估算模型将集成各种技术手段进行多尺度网络观测和大尺度机理模拟.     

散射辐射测量及其对陆地生态系统生产力影响的研究进展

全球变化,特别是大气成分变化引起的散射辐射变化已经并将继续影响陆地生态系统的生产力与碳收支。该文综述了散射辐射的影响因子及其估算方法,分析了散射辐射对植被光能利用率(light-use efficiency,LUE)、陆地生态系统生产力及其碳收支的影响过程与控制机理,在此基础上提出了未来拟加强研究的方面:1)散射辐射对植物光合作用影响的机理及其在不同时空尺度的反应;2)散射辐射及其与其他环境因子的相互作用对植物与冠层光合作用影响的定量描述;3)散射辐射及其与其他环境因子的相互作用对土壤呼吸作用的影响过程与控制机理;4)植物对散射辐射及其与其他环境因子相互作用的适应性研究;5)散射辐射及其与其他环境因子的相互作用对陆地生态系统生产力及其碳收支的影响过程与调控对策。     

植被冠层尺度生理生态模型的研究进展

随着人们对植物生命活动各个过程研究的不断深入,以植物生理过程、物理过程为基础的各种生理生态学模型逐渐发展起来,而植被冠层尺度生理生态学过程模型已成为生态系统模型的核心之一。目前植被冠层尺度的大叶模型、多层模型、二叶模型以其成熟的理论基础及对植被冠层的光合作用、蒸腾作用较为成功的模拟,得到了广泛的应用。3个模型都以光合作用-气孔导度-蒸腾作用耦合模型为基础,但又具有各自的特点。本文对3种模型的结构及特点进行了总结,并对其进行了比较,简要介绍了目前植被冠层尺度生理生态学模型的应用及存在的问题和发展状况。     

植被-大气相互作用中的气孔导度及其尺度转换

气孔导度是衡量植物和大气间水分、能量及CO2平衡和循环的重要指标,探讨气孔导度在叶片、冠层及区域尺度间的尺度转换及累积效应,对更好地认识植被与大气间的水热运移过程,合理评价植被在陆面过程中的地位和作用具有重要意义.本文着重从叶片尺度气孔导度模拟、气孔导度在冠层尺度的累积表现、冠层到区域尺度转换研究及气孔导度累积效应在陆面过程模型中的作用等4个层次总结了近期国内外研究状况,指出其中存在的异质性等问题,并就今后应加强多尺度间的同步观测提出了展望.     

基于SVAT模型的冬小麦光合作用和蒸散过程研究

在已建立的土壤－植被－大气传输（SVAT）模型中,冠层光合作用／气孔导度耦合子模型可区分遮荫叶和受光叶光合作用强度的差异;作物生长模型考虑了生长呼吸和维持呼吸,模拟与实测结果对比发现,日总蒸散量实测和模拟的根均方差（RMSD）为0.65mm,平均绝对差（MAPD）为14％;对冠层上部净光合作用率日变化过程而言,实测和模拟结果具有较好的一致性。利用模型模拟了冬小麦全生育争光合作用率和蒸散的演变过程。最后,分析了冬小麦蒸散和水分利用效率对不同最大叶面积指数,大气CO2浓度和叶片N含量的响应。     

植被光能利用率:模型及其不确定性

光能利用率（Light use efficiency： LUE）指植物截获的光能转化为化学能的效率,表示为生产力和吸收光能之比。基于LUE概念的模型对模拟预测全球变化下碳循环、植被生产力及其潜力具有重要意义。全球变化和人类活动影响给植被生产力和碳循环的评估带来了巨大挑战。系统梳理了LUE模型的不确定性并分析其原因,以期提高生产力模拟预测的准确度。分析发现LUE模型准确度仅为62%-70%且模型间差异较大（32%）,误差随着植被类型、时间尺度和空间区域的不同存在显著差别。目前计算LUE的误差是模型不确定性的关键,原因主要在于LUE与影响因素尤其是水分的关系并不清楚。一方面不能准确区分水分胁迫指标对LUE的影响机制,另一方面无法准确模拟水分等影响因素与LUE关系的时空演变特征。未来该领域研究的重要方向是发展集成样地和区域尺度的叶绿素荧光、光化学指数等研究方法,厘定LUE与影响因素特别是的水分关系,并分析其时空演变特征。     

陆地植被净第一性生产力的研究

回顾了当前国内外陆地植被净第一性生产力（ＮＰＰ） 的研究现状，分析了３ 种生产力模型（ 气候相关模型、过程模型和光能利用率模型） 在应用于全球和区域生产力研究时的长处及不足：气候相关模型在气候变化研究中应用比较多，但计算的只是潜在ＮＰＰ；过程模型着重于植物生长的生理生态过程，但过于复杂，模型中的参数不易获得；光能利用率模型因为可直接利用遥感数据成为ＮＰＰ模型发展的一个主要方面．对国内ＮＰＰ的研究及遥感手段在ＮＰＰ研究中的应用进行了分析．     

陆地植被净初级生产力计算模型研究进展

植被净初级生产力(NPP)研究是全球变化与陆地生态系统的核心内容之一。在回顾NPP模型研究的基础上,综合分析了气候模型、生态生理过程模型、光能利用率模型各自的优缺点,并对NPP模型研究做出展望。生态生理过程模型是当前陆地NPP估算研究的主要手段,而区域尺度转换则是它所面临的关键问题。近年来光能利用率模型已成为NPP估算的一种全新手段,它利用遥感所获得的全覆盖数据,使区域及全球尺度的NPP估算成为可能,但其生态学机理还有待于进一步研究。已有研究表明,“生态一遥感耦合模型”将是陆地NPP估算的主要发展方向,它融合了生态生理过程模型和光能利用率模型的优点,增强了NPP模型估算的可靠性和可操作性。     

中国陆地植被净初级生产力遥感估算

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。     

基于3S的自然植被光能利用率的时空分布特征的模拟

 光能利用率（LUE）直接影响植被各层中的能量分布和光合速率,在确定环境对光合和地上部生长分配的综合限制上十分有价值,是衡量系统功能的一个重要指标。本研究以遥感图像（TM）作为数据源,获取了影响植被LUE的重要变量——叶面积指数（LAI）;用程序语言编写了描述系统碳循环和水循环的景观尺度生态系统生产力过程模型（EPPML）,对长白山自然保护区的太阳总辐射、净初级生产力（NPP）和LUE等的季节动态和空间分布进行了模拟;并用地理信息系统（GIS）手段对空间数据进行处理、分析和显示,从而实现了将植物生理生态研究的结果从小尺度向中尺度进行拓展和转换。EPPML可以比较准确地模拟长白山自然保护区景观尺度上主要植被类型的NPP和太阳总辐射,对LUE的模拟结果也大多在我国森林的LUE范围之内,但对不同植被类型LUE的验证因实测数据不足,仅做了初步比较。模拟结果表明,长白山植被的LUE与NPP的季节进程十分近似,7月可达2.9%。春、夏、秋、冬四个季节植被LUE的模拟平均值分别为0.551%、2.680%、0.551%和0.047%。植被年LUE的模拟值平均为1.075%,在-3.272%～3.556%之间变化,阔叶红松(Pinus koraiensis)林最大（1.653%）,高山流砾滩草类最小（0.146%）。阔叶红松林的LUE虽然较高,但仍有很大的增长潜力。     

Can we measure terrestrial photosynthesis from space directly,using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.     

复杂地形草地植被碳储量遥感估算研究进展

草地生态系统是我国最大的陆地生态系统，其植被碳储量的准确评估对维护国家生态安全和指导畜牧发展有重要作用。植被生物量和草地面积是草地植被碳储量估算的关键，随着遥感技术的发展，两者估算精度和效率显著提高，先后发展出多种草地生物量遥感估算模型和土地覆被产品，并已在平坦地区取的较好估算结果。然而，复杂地形区迥异于平地的几何形态和水热分布所产生的不均一的生态系统结构和功能，给草地生物量和草地面积的遥感估算带来诸多困难，影响对草地植被碳储量的准确判定。本文在回顾国内外草地植被碳储量遥感估算方法与所需关键参数的基础上，对遥感估算复杂地形草地植被碳储量过程中所面临“遥感影像地形效应的去除和尺度选择”、“植被指数与地形指标的选取”、“过程模型植被生长参数的率定”、“草地面积估算”以及“气象数据与复杂地形上微气候的匹配”等问题进行了总结并提出相应的解决思路，以期为草地植被碳储量遥感估算模型的合理构建以及估算精度的提高提供参考。     

What is the maximum efficiency with which photosynthesis can convert solar energy into biomass?

Photosynthesis is the source of our food and fiber. Increasing world population, economic development, and diminishing land resources forecast that a doubling of productivity is critical in meeting agricultural demand before the end of this century. A starting point for evaluating the global potential to meet this goal is establishing the maximum efficiency of photosynthetic solar energy conversion. The potential efficiency of each step of the photosynthetic process from light capture to carbohydrate synthesis is examined. This reveals the maximum conversion efficiency of solar energy to biomass is 4.6% for C3 photosynthesis at 30 degrees C and today's 380 ppm atmospheric [CO2], but 6% for C4 photosynthesis. This advantage over C3 will disappear as atmospheric [CO2] nears 700 ppm.     

Sun‐induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO₂ uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun‐induced fluorescence signal on the ground and on a coarse spatial scale using space‐borne imaging spectrometers. Intermediate‐scale observations using airborne‐based imaging spectroscopy, which are critical to bridge the existing gap between small‐scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun‐induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun‐induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun‐induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun‐induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.     

Dynamic Modelling of Northeast China Transect Responses to Global Change-A Regional Vegetation Model Driven by Remote Sensing Information

A remote sensing driven dynamic simulation model was developed for terrestrial ecosystems. The model was encoded in C language under the environment of SPAMOD, a spatial simulation tool developed under MS Windows. The model was applied to Northeast China Transect to simulate the dynamics of green and non-green biomass of 12 vegetation categories as well as soil water of 3 layers. The green biomass was converted to normalized difference vegetation index (NDVI) of AVHRR remote sensing, and compared with the observed NDVI from 1986 to 1990. The model was also compared with ground measurements of biomass and productivity along the transect. Ambient CO2 concentration, monthly mean air temperature and monthly precipitation were regarded as the three basic driving variables for global change study. The model also included the effects of temperature and precipitation on sunshine fracti6n, relative humidity, radiation, soil water and eventually plant growth. For each CO2 and climatic scenario, the model was run for an equilibrium solution. The results indicated that the natural vegetation of the transect was very sensitive to variation of temperature and CO2 concentration. With CO2 remained unchanged and temperature increased by 4 CE, the induced increase in evapotranspiration could reduce the average biomass and net primary productivity (NPP) over the whole transect by 32.1% and 41.9 % respectively. In contrast, a 20 % increase in precipitation alone could lead to an increase of the average biomass and NPP by 8.1% and 13.4% respectively. Under the present climatic conditions, CO2 doubling could increase the average biomass and NPP by 12.2% and 17.1% respectively. Because of compensation between the positive effects of CO2 and precipitation increase and the negative effect of temperature increase, a comprehensive interaction among CO2 doubling, a 20% increase of precipitation and a 4 ℃ increase of temperature altogether can lead to approximately a 2% reduction in the biomass and NPP of the natural vegetation over the whole transect.     

基于通量和光谱观测的中亚热带人工针叶林光能利用效率的反演

利用光谱反射率测量的光化学植被指数（PRI）估算植被光合作用的光能利用效率（LUE）,能够更好地为生态系统总初级生产力的估算及尺度扩展提供重要的技术支撑.本研究以中国通量网（ChinaFLUX）千烟洲通量观测站为研究区域,2013年9月和12月在通量塔上测量了中亚热带人工针叶林的植被反射光谱,并获取了通量塔上同步观测的气象数据和涡度相关通量数据,对两者进行回归分析.结果表明： PRI-LUE相关关系（R²=0.20,P<0.001）优于NDVI LUE.在整个观测期内,土壤水分含量（SWC）与PRI组合的二元回归模型能够提高LUE的估算精度（日间观测R²=0.29,P<0.001;正午观测R²=0.30,P<0.01）,而在秋季,饱和水汽压差（VPD）与PRI组合的二元回归模型能较好地估算正午LUE（R²=0.448, P<0.001）,表明环境因子SWC和VPD是影响PRI-LUE关系的重要因素,不同季节的二元回归模型所选择的最佳环境变量有所不同.     

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C₃ species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.