干旱是地球上最普遍的自然扰动因子之一。干旱塑造着地球上的植被，决定着森林和草地类型，间接影响大型动物或食草动物/昆虫的分布格局。怎么强调干旱对地球生态系统的影响都不为过。目前，生态学界和植物生理学界对干旱发生的机制和影响进行了大量的研究，成果多如牛毛。但，全球尺度的干旱影响研究做的还不是很彻底。 主要是因为量化的问题，如，该怎么量化植被的分布或活动，哪个指标比较好？如何量化干旱，干旱指标有数十个之多，哪个比较好？这些问题是困扰人们进行相关研究的技术问题。 近期，伍兹霍尔研究中心(Woods Hole Research Center)的Schwalm等人基于全球植被总的第一性生产力（GPP，植被总光合能力）和量化干旱的较好指标SPEI进行了全球尺度植被响应干旱的恢复力评估。作者发现，影响干旱恢复时间首当其冲的因子是温度和降水等气象指标，像生物多样性和二氧化碳的施肥效应则排在其次。有意思的是，作者还发现，从干旱中恢复过来的时间最久的生态系统类型竟然是热带雨林和北方高纬度地区的植被（如冻原或荒漠、不毛之地等）。他们解释道，热带雨林由于很少经历干旱，对干旱的适应能力较弱，因此需要更长时间恢复；而北方稀疏植被由于低温或者降雨较少，帮助恢复的资源不充分，因此恢复能力也有较大削弱，因此也需要更长时间恢复。作者进一步解释，过热、过冷和少雨的地区恢复时间都会较长。 考虑到在本世纪干旱是一种普遍且持久的自然现象，植被恢复的时间，从作者的角度来看，会越来越长，因此，生态系统提供的服务功能会大为削弱。 目前，Schwalm等人的研究论文“Global patterns of drought recovery”已经发表在 Nature 上。 摘要： Drought, a recurring phenomenon with major impacts on both human and natural systems 1 , 2 , 3 , is the most widespread climatic extreme that negatively affects the land carbon sink 2 , 4 . Although twentieth-century trends in drought regimes are ambiguous 5 , 6 , 7 , across many regions more frequent and severe droughts are expected in the twenty-first century 3 , 7 , 8 , 9 . Recovery time—how long an ecosystem requires to revert to its pre-drought functional state—is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO 2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth’s climate system 10 ) and second, that drought impacts 11 (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.
科学家对精确测量的追求是无止境的。人们虽然在20世纪初就对冠层光合进行了测定，但精度太差，几乎不能说明任何科学问题。1951年，Swinbank使用涡度相关法进行草地显热和潜热通量的测量，这种先进的技术加上超声风速计使得冠层二氧化碳通量的测量成为可能，直到1968年人们才在美国堪萨斯州的农田进行大气边界层的观测。20世纪80年代，涡度相关法大量应用于生态系统的研究，包括冠层光合的测定，解决了很多生态学问题。 在植物生理学领域，人们可以通过对叶片光合的测定尺度推移到冠层，但这种方法的不确定性太大。对于整树碳同化而言，也有人尝试整合同位素和树干液流的方法计算冠层光合。还有对于小树的测量则可以通过整树箱法。这些方法都比较复杂，误差大。即使使用涡度相关法进行冠层碳通量的实时测定，也不能很好地捕捉植物生理的微小变动。 上世纪90年代，有人大胆地提出要在太空使用传感器监测叶绿素荧光。经过多次试验，地面验证的效果很不理想。但随着技术和仪器设备的进步，人们已经可以在大尺度上进行植被荧光的观测了，2011年成功发布了全球植被的叶绿素荧光图，而在2014年PNAS上的一篇论文再次把研究推向高潮。因此，能否通过冠层叶绿素荧光的监测直接反演冠层光合呢？答案是肯定的。近期发表在Geophys. Res. Lett.的文章“Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest”向我们展示了这种新技术的魅力。 作者Yang等发现，在一温带落叶林（哈佛森林），无论在天尺度还是季节尺度地面原位测量的荧光值与卫星遥感的和涡度相关法测定的结果相关性较好，证实了地面原位观测荧光的巨大潜力。那么，为什么这种方法要优于涡度相关法和卫星遥感呢？相比于涡度相关法，它监测的范围更广，能够直接反映植物的生理学信息。而相比于传统卫星遥感数据，如NDVI和EVI，荧光反映的是植被真实的生理学信息（或许以后就可以避免“Amazon Forest Greenness”），同时较高的时间分辨率也可以帮助人们监测作物长势、进而预测收成，而通过监测森林冠层则能够帮助科学家即时查明森林健康状况，甚至火险隐情，而这些是普通的遥感手段很难做到的。 背景知识：叶绿素荧光是植物进行光合作用时的“副产品”，在大部分条件下，叶绿素荧光与光合作用成正比，因此能帮助科学家根据荧光值反演光合。叶绿素荧光只占到叶片吸收光能的1%左右，人眼不可见，波长650-800nm，可以通过类似FluoSpec传感器监测到。
ttp://onlinelibrary.wiley.com/doi/10.1002/grl.50450/abstract 不需要地面气象观测资料的遥感蒸发模型 根据试验观测资料发现月尺度上陆面蒸发ET与总初级生产力GPP存在明显的相关关系，据此提出了一种利用遥感GPP估算ET的方法。其中 GPP估算模型为基于MODIS EVI、LST数据的 TG模型，该模型不需要地面气象观测资料，因此本文的模型可以只利用遥感资料进行陆面蒸发估算。 Remote estimation of terrestrial evapotranspiration without using meteorological data Yuting Yang 1,* , Di Long 2 , Songhao Shang 1 Article first published online: 17 JUN 2013 DOI: 10.1002/grl.50450 2013. American Geophysical Union. All Rights Reserved. Issue Geophysical Research Letters Volume 40 , Issue 12 , pages 3026–3030 , 28 June 2013 Additional Information (Hide All) How to Cite Author Information Publication History Funding Information How to Cite Yang, Y. , D. Long , and S. Shang ( 2013 ), Remote estimation of terrestrial evapotranspiration without using meteorological data , Geophys. Res. Lett. , 40 , 3026–3030, doi: 10.1002/grl.50450 . Author Information State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, USA * Corresponding author: Y. Yang, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China. ( yyt08@mails.tsinghua.edu.cn ) Publication History Issue published online: 18 JUL 2013 Article first published online: 17 JUN 2013 Accepted manuscript online: 8 APR 2013 12:44PM EST Manuscript Accepted: 5 APR 2013 Manuscript Revised: 4 APR 2013 Manuscript Received: 28 FEB 2013 View Full Article with Supporting Information (HTML) Get PDF (258K) Keywords: remote sensing; evapotranspiration; ecosystem water use efficiency; gross primary production We developed a new method to estimate terrestrial evapotranspiration (ET) from satellite data without using meteorological inputs. By analyzing observations from 20 eddy covariance tower sites across continental North America, we found a strong relationship between monthly gross primary production (GPP) and ET ( R 2 = 0.72 – 0.97), implying the potential of using the remotely sensed GPP to invert ET. We therefore adopted the Temperature-Greenness model which calculates 16 day GPP using MODIS EVI and LST products to estimate GPP and then to calculate ET by dividing GPP with ecosystem water use efficiency (the ratio of GPP to ET). The proposed method estimated 16 day ET very well by comparison with tower-based measurements ( R 2 = 0.84, p 0.001, n = 1290) and provided better ET estimates than the MODIS ET product. This suggests that routine estimation of ET from satellite remote sensing without using fine-resolution meteorological fields is possible and can be very useful for studying water and carbon cycles.
http://onlinelibrary.wiley.com/doi/10.1002/jgrg.20056/abstract 利用MODIS数据估算陆地生态系统总初级生产力GPP的新方法 基于不同类型陆地生态系统LST与EVI的关系，建立了基于矩形EVI-LST空间的GPP估算模型TGR。该模型参数具有明确的物理意义，计算中用到的多地面观测数据较少，且避免了由于解释变量相关性造成的信息重复考虑。北美地区30个生态站的模型率定、检验结果表明，本模型的精度高于目前常用的TG、GR模型。 A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery Yuting Yang 1 , Songhao Shang 1 , Huade Guan 2,3 , Lei Jiang 1 State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China School of the Environment, Flinders University, Adelaide, South Australia, Australia National Centre for Groundwater Research and Training, Adelaide, South Australia, Australia How to Cite Yang, Y. , S. Shang , H. Guan , and L. Jiang ( 2013 ), A novel algorithm to assess gross primary production forterrestrial ecosystems from MODIS imagery , J. Geophys. Res. Biogeosci. , 118 , 590 – 605 , doi: 10.1002/jgrg.20056 . Author Information * Corresponding author: Y. Yang, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China. ( yyt08@mails.tsinghua.edu.cn ) Publication History Issue published online: 9 JUL 2013 Article first published online: 30 APR 2013 Accepted manuscript online: 5 APR 2013 06:03PM EST Manuscript Accepted: 3 APR 2013 Manuscript Revised: 26 MAR 2013 Manuscript Received: 30 NOV 2012 Funded by National Key Technology RD Program of China. Grant Number: 2011BAD25B05 National Natural Science Foundation of China. Grant Number: 51279077 View Full Article (HTML) Get PDF (2815K) Keywords: gross primary production; MODIS; satellite remote sensing; light use efficiency; North America Abstract Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m 2 /16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.
标签: GPP
