Evaluating Common Land Model Energy Fluxes Using FLUXNET Data

Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model （CoLM） over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise （30 rain） to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes, A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands.

基于FLUXNET数据集对陆面模式CoLM能量通量的单点评估

模式评估是模式发展中的重要一环。本文利用来自FLUXNET2015数据集的30个站点的涡动相关系统观测数据,重点关注能量通量,对通用陆面模式(Common Land Model version 2014,CoLM2014)在不同典型下垫面的模拟能力进行评估。结果表明,模式总体上能抓住感热、潜热和净辐射通量在日、季节和年平均等不同时间尺度上的变化特征,对感热、潜热和净辐射通量都有较好的模拟能力,净辐射的模拟效果最好,潜热通量次之。季节变化模拟中,感热、潜热通量在夏季不同植被型下站点的空间离散程度大于冬季,不同站点间模拟效果相差较大,净辐射多站点标准差变化幅度要小于感热、潜热,不同站点间模拟效果偏差较小。CoLM在常绿针叶林、稀树林地、草地、农田模拟感热、潜热通量的效果相对较好,在永久湿地、落叶阔叶林下模拟感热通量较差。本研究对CoLM2014在未来的改进和发展中提供了有用的参考。

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0.05~0.08μmol CO_2·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Tmax为38~48℃,Topt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FLUXNET的CLM模型生态系统呼吸模拟验证

准确估算陆地生态系统呼吸(Ecosystem respiration,RE)对全球陆地生态系统碳收支研究具有重要意义.模型模拟是估算陆地RE变化的一种常用手段.然而目前陆地生态系统过程模型的RE模拟尚未得到充分验证.基于耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的通用陆面模型(Community land model,CLM)RE模拟结果和全球通量网(FLUXNET)66个站点的涡度相关通量观测数据(277条站点年数据)评估CLM模型对RE的模拟效果.结果表明:(1)在空间尺度上,CLM低估了高纬度站点RE,高估了低纬度站点RE,但高纬度低估量更大导致空间格局整体低估(相对误差为-3.56%).(2)在时间尺度上,CLM模型基本捕捉了RE的年际和季节变化,相关系数分别为0.60(P < 0.001)和0.63(P < 0.001);CLM低估年尺度和月尺度的RE(以C计),绝对误差分别是-182.21 g m-2 a-1、-120.16 g m-2 mon-1,相对误差分别是-17.84%、-10.60%.(3)CLM模型对不同植被功能型的RE模拟效果不同,由优及差依次为混交林、常绿针叶林、草地、农田、落叶阔叶林、常绿阔叶林.本研究在时空尺度上量化了CLM模型的生态系统呼吸模拟误差,并分析了土壤呼吸Q10和MRbase参数以及土壤碳库模拟等因素的影响,可为CLM模型的生态系统呼吸模块参数优化提供依据,进而提升其模拟精度.

近30年全球干旱半干旱区的蒸散变化特征

全球变暖加剧了气候系统能量和水分循环相互作用的变化,水分平衡变化导致极端旱涝事件频发。地表蒸散是能量水分循环的重要过程,是理解气候变化的关键环节。本文基于1982~2011年FLUXNET-MTE观测资料和ERA-Interim再分析资料,分析了全球干旱半干旱区蒸散的时空变化特征及典型区域的变幅、趋势和季节变化。结果表明:(1)干旱半干旱区多年平均蒸散量小于300 mm。冬季蒸散量最小,夏季最大且变率也最强。1990年代前后,干旱半干旱区蒸散发生了明显的年代际转变,暖季的年代际差异尤为明显。(2)近30年来,东半球干旱半干旱区蒸散量呈增加趋势,西半球呈减小趋势。典型区域来看,南非呈显著增加趋势[25.14 mm(10 a)-1],美国西南部呈显著减小趋势[-19.86 mm(10 a)-1];萨赫勒、中国北部和澳大利亚呈增加趋势,阿根廷及智利南部呈减小趋势。(3)蒸散变化与温度、降水的变化联系密切,三者具有相似的年循环变化,但三者间相关性在干旱半干旱区具有显著的差异性。

新型遥感卫星土壤水分产品在不同地表覆盖下的精度验证与分析

近年来,土壤水分遥感卫星数据被广泛应用在气候、水文、农业等领域的研究中,微波土壤水分数据产品的适用性分析显得越来越重要。本研究分别以美国国家航空航天局的FLUXNET2015数据集和欧洲航天局发起并进行集中数据托管的ISMN网络为基准,运用统计学的方法(偏差、均方根误差和时间序列相关系数),在2012年7月至2014年12月,以IGBP(国际地圈生物圈计划)为标准的地表覆盖类型进行分类,在全球范围内的FLUXNET站点对SMOS-L3-SM和AMSR2-LPRM-SM两种遥感土壤水分产品与实测数据进行了对比验证,并在2016年1月至2017年12月,以30 m全球地表覆盖产品(GlobeLand30)为标准的地表覆盖类型进行分类,在全球范围内的ISMN站点对SMAP-L3-SM和AMSR2-LPRM-SM两种遥感土壤水分产品与实测数据进行了对比验证。结果表明:三种遥感土壤水分产品在草地等低矮植被类型低矮稀疏植被覆盖区域的土壤水分产品反演精度均要高于高大茂密植被覆盖的区域,且SMOS和AMSR2产品在地表覆盖为针叶林下反演精度较低,SMAP和AMSR2产品在地表覆盖为湿地下反演精度较低,SMAP产品的反演精度要高于AMSR2产品。

生态系统呼吸温度敏感性的分布及影响因素

生态系统呼吸的温度敏感性(Q10)是表征气候变暖-陆地碳循环反馈作用的一个关键参数,然而其全球分布格局及其对关键环境因子变化的响应仍存在很大的不确定性。该研究基于全球199个站点的碳通量和气象数据,形成了一套全球生态系统呼吸温度敏感性的数据集。分析结果表明:全球生态系统呼吸Q10的平均值为(2.69±1.26),不同生态系统间存在显著差异,其中湿地的Q10值最高(3.37±1.62),灌丛最低(2.49±1.60)。除多树热带草原外,各生态系统的Q10值均随纬度的增加而增大,随年均气温、年降水量及饱和水气压差的增大而减小,这种差异与环境因子对Q10产生影响的阈值范围有关。研究结果突出了生态系统呼吸Q10的空间变异及对环境因子的响应在生态系统碳循环研究中的重要性,并建议在陆地碳收支研究中除了考虑Q10的总体变化还应兼顾不同生态系统类型间的差异。

The seasonal variability of an air-sea heat flux in the northern South China Sea

The seasonal variabilities of a latent-heat flux （LHF）, a sensible-heat flux （SHF） and net surface heat flux are examined in the northern South China Sea （NSCS）, including their spatial characteristics, using the in situ data collected by ship from 2006 to 2007. The spatial distribution of LHF in the NSCS is mostly controlled by wind in summer and autumn owing to the lower vertical gradient of air humidity, but is influenced by both wind and near-surface air humidity vertical gradient in spring and winter. The largest area-averaged LHF is in autumn, with the value of 197.25 W/m 2 , followed by that in winter; the third and the forth are in summer and spring, respectively. The net heat flux is positive in spring and summer, so the NSCS absorbs heat; and the solar shortwave radiation plays the most important role in the surface heat budget. In autumn and winter, the net heat flux is negative in most of the observation region, so the NSCS loses heat; and the LHF plays the most important role in the surface heat budget. The net heating is mainly a result of the offsetting between heating due to the shortwave radiation and cooling due to the LHF and the upward （outgoing） long wave radiation, since the role of SHF is negligible. The ratio of the magnitudes of the three terms （shortwave radiation to LHF to long-wave radiation） averaged over the entire year is roughly 3：2：1, and the role of SHF is the smallest.

DLM-FvCB模型光合能力关键参数季节性参数化方案及模拟效果评估

选取包含9种植被功能型的15个通量塔观测数据(60站点年),采用控制变量法,分别在月尺度和年尺度上对25℃时最大羧化速率(V_(cmax25))进行优化,并利用未参与参数反演的站点年数据集定量评估这2种参数化方案的模拟效果。研究发现:①在参数的季节变异方面:9种植被功能型的V_(cmax25)均呈现明显的季节性波动。V_(cmax25)的波动幅度为:冬季>秋季>春季>夏季,不同植被类型季节变化幅度相差不大,而寒带植被V_(cmax25)季节变化幅度接近温带植被的2倍;②对生态系统初级生产力(GPP)估算的影响方面:V_(cmax25)季节性参数化方案显著提高了GPP的模拟能力和模拟精度,其中森林和灌木冬季提升最显著(R2提高了35.7%,RMSE降低了23.24%),春秋季次之,夏季最小。然而,对于C3草地,DLM无论采取V_(cmax25)季节性参数化方案还是年尺度参数化方案,均为系统低估GPP。

Sensitivity of Near Real-time MODIS Gross Primary Productivity in Terrestrial Forests Based on Eddy Covariance Measurements

As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.

Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites

Variations in net ecosystem exchange(NEE)of carbon dioxide,and the variables influencing it,at woodland sites over multiple years determine the long term performance of those sites as carbon sinks.In this study,weekly-averaged data from two AmeriFlux sites in North America of evergreen woodland,in different climatic zones and with distinct tree and understory species,are evaluated using four multi-linear regression(MLR)and seven machine learning(ML)models.The site data extend over multiple years and conform to the FLUXNET2015 pre-processing pipeline.Twenty influencing variables are considered for site CA-LP1 and sixteen for site US-Mpj.Rigorous k-fold cross validation analysis verifies that all eleven models assessed generate reproducible NEE predictions to varying degrees of accuracy.At both sites,the best performing ML models(support vector regression(SVR),extreme gradient boosting(XGB)and multi-layer perceptron(MLP))substantially outperform the MLR models in terms of their NEE prediction performance.The ML models also generate predicted versus measured NEE distributions that approximate cross-plot trends passing through the origin,confirming that they more realistically capture the actual NEE trend.MLR and ML models assign some level of importance to all influential variables measured but their degree of influence varies between the two sites.For the best performing SVR models,at site CA-LP1,variables air temperature,shortwave radiation outgoing,net radiation,longwave radiation outgoing,shortwave radiation incoming and vapor pressure deficit have the most influence on NEE predictions.At site US-Mpj,variables vapor pressure deficit,shortwave radiation incoming,longwave radiation incoming,air temperature,photosynthetic photon flux density incoming,shortwave radiation outgoing and precipitation exert the most influence on the model solutions.Sensible heat exerts very low influence at both sites.The methodology applied successfully determines the relative importance of influential variables in determining weekly NEE trends at both conifer woodland sites studied.

Machine Learning and Pattern Analysis Identify Distinctive Influences from Long-term Weekly Net Ecosystem Exchange at Four Deciduous Woodland Locations

A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.

中国陆地生态系统通量观测研究网络(ChinaFLUX)的研究进展及其发展思路

涡度相关技术经过长期的理论发展和技术进步,已经成为直接测定陆地生态系统与大气间的CO2和水热通量的重要方法．随着涡度相关通量观测在全球范围内的广泛开展,各区域、国家以及国际通量观测研究网络(FLUXNET)也应运而生．在过去10年里,通量观测研究在探讨全球陆地生态系统碳循环和水循环过程及环境控制机理、揭示陆地生态系统碳收支的时空格局、寻找“未知碳汇”等方面取得了显著的成果,也为资源、生态和环境等科学领域的国际交流创造了理想的合作研究平台．随着通量观测研究的不断深入,今后国际通量界将加强引进和开发新的观测技术,扩展通量观测的应用领域,尝试运用通量观测数据来协助研究有关生物地理学、生物地球化学、生态水文学、气象／气候学、遥感和全球碳循环模型等领域的科学问题．中国陆地生态系统通量观测研究网络(ChinaFLUX)是FLUXNET的重要组成部分,经过3年多的连续观测和研究已在涡度相关通量观测技术和方法、典型陆地生态系统碳水交换过程及其环境响应机理、生态系统碳水通量模型开发等方面取得了一系列重要进展．研究发现中国的主要森林生态系统在2003～2005年度都为大气CO2的汇,青藏高原上的高寒草甸生态系统也表现为较弱的碳汇,而封育的内蒙古半干旱羊草草原却表现为弱的碳源;在大的空间尺度上,温度和水分是决定陆地生态系统碳收支的关键环境因子．ChinaFLUX的发展思路拟以典型生态系统通量的联网观测与陆地样带研究相结合为技术途径,开展多尺度、多过程、多途径、多学科的综合集成观测,重点探讨生态系统的水、碳、氮循环过程机理及其耦合关系．

利用Landsat TM数据和地面观测数据验证GLASS反照率产品

Global LAnd Surface Satellite Products System(GLASS)反照率产品基于Angular Bin(AB)算法,仅使用单一观测角度的地表或大气层顶反射率数据就能较为准确地反演地表宽波段反照率,具有较高的时间分辨率,可以反映降雪、融雪、收割等状况下地表反照率的快速变化。遵循"一检两恰"的验证流程对这一反照率产品进行验证,首先使用FLUXNET站点验证数据对AB算法反演的Landsat Thematic Mapper(TM)高分辨地表反照率数据进行验证,再将TM高分辨反照率聚合到GLASS像元尺度对GLASS反照率产品进行验证。挑选FLUXNET的5个站点,筛选无云条件下的TM高分辨率影像,共获得103组有效验证数据。验证结果表明,GLASS反照率产品具有较高的精度,总体误差约为0.0163,可以满足大多数应用的精度需求。

3种反演算法的地表反照率遥感产品对比分析

地表反照率对于地表能量平衡和全球气候变化具有重要的影响。近年来,地表反照率产品已经取得了较为广泛的应用,但是仍存在时间分辨率低和数据缺失等问题。本文基于均匀站点观测数据与MODIS的MCD43B3产品验证了GLASS(Global LAnd Surface Satellite)地表反照率产品的3种算法及其对应的反照率产品:AB1(ABD01)、AB2(ABD03)和STF(ABD06)。并分析比较了GLASS反照率初级产品到最终产品的质量提升。结果表明:最终产品ABD06与地面观测数据符合最好,RMSE为0.050,R2为0.788;同时,ABD06与MCD43B3反照率产品一致性最好,RMSE优于0.03,R2达到0.9,且ABD06有效数据最多(ABD01为4290,ABD03为3296,ABD06为5507),比较适合生产长时间序列的反照率产品。大量数据的统计结果还表明:3种GLASS产品80%以上的数据与地面观测数据的偏差均能满足气候模式对反照率的精度要求(0.02—0.05)。

Recent progress and future directions of ChinaFLUX

The eddy covariance technique has emerged as an important tool to directly measure carbon dioxide, water vapor and heat fluxes between the terrestrial ecosystem and the atmosphere after a long history of fundamental research and technological developments. With the realization of regional networks of flux measurements in North American, European, Asia, Brazil, Australia and Africa, a global-scale network of micrometeorological flux measurement (FLUXNET) was established in 1998. FLUXNET has made great progresses in investigating the environmental mechanisms controlling carbon and water cycles, quantifying spatial-temporal patterns of carbon budget and seeking the "missing carbon sink" in global terrestrial ecosystems in the past ten years. The global-scale flux measurement also built a platform for international communication in the fields of resource, ecology and environment sciences. With the continuous development of flux research, FLUXNET will introduce and explore new techniques to extend the application fields of flux measurement and to answer questions in the fields of bio-geography, eco-hydrology, meteorology, climate change, remote sensing and modeling with eddy covariance flux data. As an important part of FLUXNET, ChinaFLUX has made significant progresses in the past three years on the methodology and technique of eddy covariance flux measurement, on the responses of CO2 and H2O exchange between the terrestrial ecosystem and the atmosphere to environmental change, and on flux modeling development. Results showed that the major forests on the North-South Transect of Eastern China (NSTEC) were all carbon sinks during 2003 to 2005, and the alpine meadows on the Tibet Plateau were also small carbon sinks. However, the reserved natural grassland, Leymus chinensis steppe in Inner Mongolia, was a carbon source. On a regional scale, temperature and precipitation are the primary climatic factors that determined the carbon balance in major terrestrial ecosystems in China. Finally, the current research emphasis and future directions of ChinaFLUX were presented. By combining flux network and terrestrial transect, ChinaFLUX will develop integrated research with multi-scale, multi-process, multi-subject observations, placing emphasis on the mechanism and coupling relationships between water, carbon and nitrogen cycles in terrestrial ecosystems.

国际重大研究计划与中国生态系统研究展望——中国生态大讲堂百期学术演讲暨2014年春季研讨会评述

以"传播新知识、交流新思想、展示新成果"为宗旨的中国生态大讲堂百期学术演讲暨2014年春季研讨会于2014年4月25日在北京举行。本次研讨会以"国际重大研究计划与中国生态系统研究展望"为主题,邀请秦大河、姚檀栋、傅伯杰、崔鹏4位中国科学院院士和马克平、于贵瑞、张佳宝、秦伯强4位知名专家作了主题报告。8位报告人分别介绍了政府间气候变化专门委员会(IPCC)、未来地球(Future Earth)、第三极环境(Third Pole Environment)、国际长期生态监测研究网络(ILTER)、生物多样性和生态系统服务政府间科学—政策平台(IPBES)、生物多样性计划(DIVERSITAS)、通量观测研究计划(FluxNet)等国际重大研究计划的进展和趋势,并就山洪泥石流风险分析与管理、碳通量空间格局及生物地理生态学机制、农田地力提升和湖泊富营养化治理等领域的前沿科学问题和研究进展作了系统阐释。基于8位报告人的演讲,本文评述了8个报告的主要内容和亮点工作,分析了国际生态环境领域重大国际研究计划的发展趋势及其对中国生态系统研究的启示,讨论了中国相关领域的科学研究方向和主要问题。

MODIS V006和V005全球反照率产品精度对比分析

反照率作为一种非常重要的地表能量平衡、全球变化研究的参数,在众多研究领域中得到了广泛的应用,到目前为止已经有多种全球范围的反照率产品进行业务化生产和发布,针对不同反照率产品质量评价的研究也变得愈加重要。MODLAND团队在MODIS V005反照率产品反演算法的基础上通过改进16天周期内观测数据加权的方法生产出新版本的反照率产品MODIS V006。本文针对MODIS两个版本V005及V006的反照率产品,利用FLUXNET地面站点数据,比较验证两个版本反照率的总体精度以及在不同地表类型条件下的精度差异,同时通过交叉验证的方法分析二者的差异及稳定性。验证结果表明,MODIS V006反照率产品虽然在全反演高质量的数据比例上较V005有所下降,但是在同等条件下V006在提高时间分辨率的同时其精度也有所提高,在不同的地表类型条件下精度也优于V005,且在时间序列分布上具有稳定比例的高质量数据,可以满足大多数应用的精度需求。

Linking observation,modelling and satellite-based estimation of global land evapotranspiration

Evapotranspiration(ET)is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance.In this paper,the exited actual ETmain datasets in global scale,and the global ET modeling and estimates were focused on discussion.The Source energy balance(SEB)models,empirical models and other process-based models are summarized.Accuracy for ET estimates by SEBmodels highly depends on accurate surface temperature retrieval,and SEB models are hard to apply in large heterogeneous surface.The Penman-Monteith(PM)equations are thought to be with considerable sound mechanism.However,it involves large number of parameters,which are not all global available.A simplified PM equation by Priestley and Taylor(PT)is found to perform well on well-watered surface.For both PM and PT equations in estimating ET,the key is to consider the constraint from surface resistance primarily water stress.Empiricalmodels are simple but the accuracy of which highly depends on training samples.Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs;However,finding the most proper way to estimate global ET remains problematic.Several reasons for this issue are also analyzed in this review.