地球科学进展 ›› 2008, Vol. 23 ›› Issue (8): 884 -894. doi: 10.11867/j.issn.1001-8166.2008.08.0884

生态学研究 上一篇    下一篇

生态系统碳通量估算中耦合涡度协方差与遥感技术研究进展
严燕儿,赵斌,郭海强,吴千红   
  1. 复旦大学生物多样性科学研究所,生物多样性与生态工程教育部重点实验室,上海 200433
  • 收稿日期:2008-02-29 修回日期:2008-05-19 出版日期:2008-08-10
  • 通讯作者: 赵斌(1969-),男,湖北钟祥人,副教授,主要从事景观生态学与生态系统学的研究. E-mail:zhaobin@fudan.edu.cn
  • 基金资助:

    国家重点基础研究发展计划项目“湿地系统水生态功能退化及修复机理”(编号:2006CB403305);教育部2006年新世纪优秀人才支持计划项目“人为干扰和自然过程对长江河口湿地生态系统碳通量的影响”(编号:NCET-06-0364)资助.

On the Coupling between Eddy Covariance and Remote Sensing Techniques in Ecosystem Carbon Flux Estimation

Yan Yaner,Zhao Bin,Guo Haiqiang,Wu Qianhong   

  1. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China
  • Received:2008-02-29 Revised:2008-05-19 Online:2008-08-10 Published:2008-08-10

陆地生态系统CO2和水热通量的长期观测研究一直是国际上关注的热点问题。截止目前,利用微气象学原理的涡度协方差技术是唯一能直接测定生物圈与大气间物质与能量通量的标准方法,成为国际通量观测网络的主要技术。但是涡度协方差技术的测定仍然是一种小尺度观测方法,其观测结果难于直接外推到更大尺度。同时,缺乏区域、跨尺度生态系统及其时空动态观测数据一直是限制碳循环研究的主要障碍,而遥感技术的发展可望在不远的将来使大尺度、高分辨生态系统变化的长期定量观测成为可能。这些问题在当今集中体现在如何建立通量—遥感的跨尺度观测体系,并有效地将有限的通量站点测量数据与大尺度遥感资料以及生态模型有机地结合。总结过去耦合涡度协方差技术与遥感技术的工作,主要在以下3个层面展开:①涡度协方差技术与遥感技术对碳通量估算的相互验证;②涡度协方差技术为遥感反演提供地面参数;③遥感观测解译辅助分析通量贡献区(footprint)。集中在这3个方面进行探讨,通过总结各方面的研究特点与进展,可望为未来在这个领域开展工作理顺思路。

Long term observation and research on carbon, water and heat fluxes of terrestrial ecosystem has been the global hotspot. To date, micrometeorology approach based on eddy covariance technique has been regarded as the only direct measurement of material and energy fluxes between atmosphere and biosphere, and has been the key technology of the international flux network. However, eddy covariance technique is a small scale observation method, and the results acquired from the method are difficult to directly scale up to larger scales. Moreover, the lack of observation data from regional, cross-scale ecosystems and their temporal-spatial dynamic remains an important issue limiting the progress of carbon cycle researches. With the development of remote sensing technology, it is possible to make the long term quantitative observation in large scale and high resolution ecosystems in the near future. These problems currently focus on how to establish the cross-scale observation system coupling carbon flux and remote sensing techniques, and how to link limited observation of carbon flux stations to large scale remotely sensed data and ecological models. The past works on coupling eddy covariance technique and remote sensing technology are summarized. The following three aspects were focused: ①Cross validation on the carbon flux estimation between eddy covariance and remote sensing technology; ②Eddy covariance provides ground parameters for remote sensing inversion; ③Remote sensing based eddy footprint analysis. Through discussion on these aspects and summary about their features and progress, it helps to sort out the ideas toward future work in this field.

中图分类号: 

[1] Thomas C DWilliams S ECameron Aet al. Biodiversity conservation: Uncertainty in predictions of extinction risk/Effects of changes in climate and land use/Climate change and extinction riskReply[J]. Nature2004430:6995.

[2] Thomas C DCameron AGreen R Eet al. Extinction risk from climate change [J]. Nature20044276 970:145-148. 

[3] Schimel D SHouse J IHibbard K Aet al. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems [J]. Nature20014146 860: 169-172. 

[4] Hoffert M ICaldeira KBenford Get al. Advanced technology paths to global climate stability: Energy for a greenhouse planet [J]. Science20022985 595: 981-987. 

[5] Yu Guirui. Global Change Carbon Cycle and Storage in Terrestrial Ecosystem [M]. BeijingChina Meteorological Press2003.[于贵瑞.全球变化与陆地生态系统碳循环和碳蓄积[M]. 北京:气象出版社,2003.]

[6] Kang SRunning S WLim J Het al. A regional phenology model for detecting onset of greenness in temperate mixed forestsKorea: An application of MODIS leaf area index [J]. Remote Sensing of Environment200386: 232-242. 

[7] Baldocchi DFalge EGu L Het al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxidewater vaporand energy flux densities [J]. Bulletin of the American Meteorological Society20018211: 2 415-2 434.

[8] Baldocchi D D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Pastpresent and future [J]. Global Change Biology200394: 479-492. 

[9] Running S WBaldocchi D DTurner D Pet al. A global terrestrial monitoring network integrating tower fluxesflask samplingecosystem modeling and EOS satellite data [J]. Remote Sensing of Environment1999701: 108-127. 

[10] Yu GuiruiSun Xiaoming. Principles of Flux Measurement in Terrestrial Ecosystems [M]. Beijing: Higher Education Press2006.[于贵瑞,孙晓明. 陆地生态系统通量观测的原理与方法[M]. 北京:高等教育出版社,2006.]

[11] Nagler P LCleverly JGlenn Eet al. Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data [J]. Remote Sensing of Environment2005941: 17-30. 

[12] Yu G RZhang L MSun X Met al. Advances in carbon flux observation and research in Asia [J]. Science in ChinaSeries D),200548: 1-16. 

[13] Veroustraete FSabbe HRasse D Pet al. Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors [J]. International Journal of Remote Sensing2004254: 769-792. 

[14] Amthor J SChen J MClein J Set al. Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models: Intermodel comparisons and relationships to field measurements [J]. Journal of Geophysical Research Atmospheres2001106D24: 33 623-33 648.

[15] Wilson KGoldstein AFalge Eet al. Energy balance closure at FLUXNET sites[J]. Agricultural and Forest Meteorology20021131/4: 223-243. 

[16] Potter CKlooster SHiatt Set al. Methane emissions from natural wetlands in the United States: Satellite-derived estimation based on ecosystem carbon cycling [J]. Earth Interactions200610:1-12. 

[17] Potter C SRanderson J TField C Bet al. Terrestrial ecosystem production: A process model based on global satellute and surface data [J]. Global Biogeochemical Cycles199374: 811-842. 

[18] Chen J MLiu JCihlar Jet al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications [J]. Ecological Modelling19991242/3: 99-119. 

[19] Adiku S G KReichstein MLohila Aet al. PIXGRO: A model for simulating the ecosystem CO2 exchange and growth of spring barley [J]. Ecological Modelling20061903/4: 260-276. 

[20] Kimball J SRunning S WSaatchi S S. Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid-scale land cover complexity [J]. Journal of Geophysical Research-Atmospheres1999104D22: 27 789-27 801.

[21] Kimball J SWhite M ARunning S W. BIOME-BGC simulations of stand hydrologic processes for BOREAS [J]. Journal of Geophysical Research199710224: 29 043-29 052. 

[22] Zhang R HSun X MZhu Z Let al. A remote sensing model of CO2 flux for wheat and studying of regional distribution [J]. Science in ChinaSeries D),1999423: 325-332. 

[23] Houborg R MSoegaard H. Regional simulation of ecosystem CO2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite dataapplication to ZealandDenmark [J]. Remote Sensing of Environment2004931/2: 150-167.

[24] Boegh ESoegaard H. Remote sensing based estimation of evapotranspiration rates [J]. International Journal of Remote Sensing20042513: 2 535-2 551. 

[25] Reich P BTurner D PBolstad P. An approach to spatially distributed modeling of net primary productionNPP at the landscape scale and its application in validation of EOS NPP products [J]. Remote Sensing of Environment1999701: 69-81.

[26] Chiesi MMaselli FBindi Met al. Modelling carbon budget of Mediterranean forests using ground and remote sensing measurements [J]. Agricultural and Forest Meteorology20051351/4: 22-34. 

[27] Fuentes D AGamon J ACheng Y Fet al. Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS [J]. Remote Sensing of Environment20061033: 312-323.

[28] Wythers K RReich P BTurner D P. Predicting leaf area index from scaling principles: Corroboration and consequences [J]. Tree Physiology20032317: 1 171-1 179. 

[29] Heinsch F AZhao M SRunning S Wet al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations [J]. IEEE Transactions on Geoscience and Remote Sensing2006447:1 908-1 925.

[30] Zhao MHeinsch F ANemani R Ret al. Improvements of the MODIS terrestrial gross and net primary production global data set [J]. Remote Sensing of Environment2005952: 164-176. 

[31] Waring R HLaw B EGoulden M Let al. Scaling gross ecosystem production at harvard forest with remote-sensing-A comparison of estimates from a constrained quantum-use efficiency Model and eddy-correlation [J]. Plant Cell and Environment19951810: 1 201-1 213.

[32] Turner D PUrbanski SBremer Det al. A cross-biome comparison of daily light use efficiency for gross primary production [J]. Global Change Biology200393:383-395. 

[33] Turner D PRitts W DCohen W Bet al. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring [J]. Global Change Biology2005114: 666-684.

[34] Turner D POllinger SSmith M Let al. Scaling net primary production to a MODIS footprint in support of Earth observing system product validation [J]. International Journal of Remote Sensing20042510:1 961-1 979.

[35] Cohen W BMaiersperger T KTurner D Pet al. MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere [J]. IEEE Transactions on Geoscience and Remote Sensing2006447: 1 843-1 857. 

[36] Leuning RCleugh H AZegelin S Jet al. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: Measurements and comparison with MODIS remote sensing estimates [J]. Agricultural and Forest Meteorology20051293/4: 151-173.

[37] Coops N CJassal R SLeuning Ret al. Incorporation of a soil water modifier into MODIS predictions of temperate Douglas-fir gross primary productivity: Initial model development [J]. Agricultural and Forest Meteorology20071473/4: 99-109.

[38] Walsh S JButler D RMalanson G P. An overview of scalepatternprocess relationships in geomorphology: A remote sensing and GIS perspective [J]. Geomorphology1998213/4: 183-205. 

[39] Igarashi TShimada MRosenqvist Aet al. Preliminary study on data sets of ADEOS-II and ALOS dedicated to terrestrial carbon observation [C]CalibrationCharacterization of Satellite SensorsPhysical Parameters Derived from Satellite Data. Kidlington: Pergamon Elsevier Science Ltd2003:2 147-2 152.

[40] Gao QYu MYang X Set al. Scaling simulation models for spatially heterogeneous ecosystems with diffusive transportation [J]. Landscape Ecology2001164: 289-300. 

[41] Lakshmi VZehrfuhs D. Normalization and comparison of surface temperatures across a range of scales [J]. IEEE Transactions on Geoscience and Remote Sensing20024012: 2 636-2 646. 

[42] Cheng YGamon J AFuentes D Aet al. A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of fieldAVIRIS and MODIS data [J]. Remote Sensing of Environment2006103: 369-378. 

[43] Anderson M CKustas W PNorman J M. Upscaling and downscaling—A regional view of the soil-plant-atmosphere continuum [J]. Agronomy Journal2003956: 1 408-1 423. 

[44] Anderson M CNeale C M ULi Fet al. Upscaling ground observations of vegetation water contentcanopy heightand leaf area index during SMEX02 using aircraft and Landsat imagery [J]. Remote Sensing of Environment2004924: 447-464.

[45] Gamon J ACheng YClaudio Het al. A mobile tram system for systematic sampling of ecosystem optical properties [J]. Remote Sensing of Environment2006103: 246-254. 

[46] Anderson M CKustas W PNorman J M. Upscaling flux observations from local to continental scales using thermal remote sensing [J]. Agronomy Journal2007991: 240-254.

[47] Gioli BMiglietta FDe Martino Bet al. Comparison between tower and aircraft-based eddy covariance fluxes in five European regions [J]. Agricultural and Forest Meteorology20041271/2: 1-16. 

[48] Brunsell N AGillies R R. Length scale analysis of surface energy fluxes derived from remote sensing [J]. Journal of Hydrometeorology200346: 1 212-1 219. 

[49] Running S WNemani R RHeinsch F Aet al. A continuous satellite-derived measure of global terrestrial primary production [J]. Bioscience2004546: 547-560. 

[50] Gamon J ARahman A FDungan J Let al. Spectral NetworkSpecNet-What is it and why do we need it? [J]. Remote Sensing of Environment20061033: 227-235. 

[51] Wang QTenhunen JFalge Eet al. Simulation and scaling of temporal variation in gross primary production for coniferous and deciduous temperate forests [J]. Global Change Biology2004101: 37-51. 

[52] Hagen S CBraswell B HLinder Eet al. Statistical uncertainty of eddy flux-based estimates of gross ecosystem carbon exchange at Howland ForestMaine [J]. Journal of Geophysical Research Atmospheres2006111: D08S03. 

[53] Gilmanov T GJohnson D ASaliendra N Z. Growing season CO2 fluxes in a sagebrush-steppe ecosystem in Idaho: Bowen ratio/energy balance measurements and modeling [J]. Basic and Applied Ecology200342: 167-183.

[54] Falge EReth SBruggemann Net al. Comparison of surface energy exchange models with eddy flux data in forest and grassland ecosystems of Germany [J]. Ecological Modelling20051882/4: 174-216. 

[55] Xiao X MHollinger DAber Jet al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest [J]. Remote Sensing of Environment2004894: 519-534. 

[56] Papale DReichstein MAubinet Met al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: Algorithms and uncertainty estimation [J]. Biogeosciences200634: 571-583. 

[57] Jiang H LFeingold G. Effect of aerosol on warm convective clouds: Aerosol-cloud-surface flux feedbacks in a new coupled large eddy model [J]. Journal of Geophysical Research-Atmospheres2006111:D01202. 

[58] Lu L XShuttleworth W J. Incorporating NDVI-derived LAI into the climate version of RAMS and its impact on regional climate [J]. Journal of Hydrometeorology200233: 347-362. 

[59] Zhang LWylie BLoveland Tet al. Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands [J]. Remote Sensing of Environment20071062: 173-189. 

[60] Boegh ESoegaard HChristensen J Het al. Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: Application to Denmark [J]. International Journal of Remote Sensing20042513: 2 553-2 574.

[61] Turner WSpector SGardiner Net al. Remote sensing for biodiversity science and conservation [J]. Trends in Ecology & Evolution2003186: 306-315. 

[62] Turner D PRitts W DZhao M Set al. Assessing interannual variation in MODIS-based estimates of gross primary production [J]. IEEE Transactions on Geoscience and Remote Sensing2006447: 1 899-1 907. 

[63] Turner D PRitts W DCohen W Bet al. Evaluation of MODIS NPP and GPP products across multiple biomes [J]. Remote Sensing of Environment20061023/4: 282-292. 

[64] Sims D ALuo H YHastings Set al. Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem [J]. Remote Sensing of Environment20061033: 289-303. 

[65] Schwalm C RBlack T AAmiro B Det al. Photosynthetic light use efficiency of three biomes across an east-west continental-scale transect in Canada [J]. Agricultural and Forest Meteorology20061401/4: 269-286.

[66] Xiao X M. Light absorption by leaf chlorophyll and maximum light use efficiency [J]. IEEE Transactions on Geoscience and Remote Sensing2006447:1 933-1 935. 

[67] Sims D ARahman A FCordova V Det al. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems [J]. Journal of Geophysical Research-Biogeosciences2006111G04015),doi:10.1029/2006JG000162.

[68] Gower S TKuckarik C JNorman J M. Direct and indirect estimation of leaf area indexfAPAR and net primary production of terrestrial ecosystems [J]. Remote Sensing of Environment199970: 29-51. 

[69] Turner D PGower S TCohen W Bet al. Effects of spatial variability in light use efficiency on satellite-based NPP monitoring [J]. Remote Sensing of Environment2002803: 397-405. 

[70] Sims D ARahman A FCordova V Det al. Midday values of gross CO2 flux and light use efficiency during satellite overpasses can be used to directly estimate eightday mean flux [J]. Agricultural and Forest Meteorology20051311/2: 1-12.

[71] Xiao X MZhang Q YHollinger Det al. Modeling gross primary production of an evergreen needleleaf forest using MODIS and climate data [J]. Ecological Applications2005153: 954-969. 

[72] Ruimy AJavis PSaugier Bet al. CO2 flux over plant canopies and solar radiation: A review [J]. Advances in Ecological Research199526: 1-68. 

[73] Ruimy ADedieu GSaugier B. TURC: A diagnostic model of continental gross primary productivity and net primary productivity [J]. Global Biogeochemical Cycles1996102: 269-285. 

[74] Asner G PWessman C ASchimel D Set al. Variability in leaf and litter optical properties: Implications for BRDF model inversions using AVHRRMODISand MISR [J]. Remote Sensing of Environment1998633: 243-257. 

[75] Hanan N P. Enhanced two-layer radiative transfer scheme for a land surface model with a discontinuous upper canopy [J]. Agricultural and Forest Meteorology20011094: 265-281. 

[76] Hanan N PBurba GVerma S Bet al. Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption [J]. Global Change Biology200286: 563-574. 

[77] Oliphant ASusan CGrimmond Bet al. Local-scale heterogeneity of photosynthetically active radiation PAR),absorbed PAR and net radiation as a function of topographysky conditions and leaf area index [J]. Remote Sensing of Environment2006103: 324-337.

[78] Kang SRunning S WZhao Met al. Improving continuity of MODIS terrestrial photosynthesis products using an interpolation scheme for cloudy pixels [J]. International Journal of Remote Sensing2005268: 1 659-1 676.

[79] Zhao BinChen Shiping. The application of eddy covariance technology in ecology [C]Chen JiquanLi BoMa Zhijunet aleds. Challenges Facing Ecologists: Question and Approaches. Beijing: Higher Education Press2005:68-102.[赵斌,陈世苹. 涡度协方差技术在生态学中的应用[C]陈吉泉,李博,马志军,等编. 生态学家面临的挑战——问题与途径. 北京:高等教育出版社,2005:68-102.]

[80] Göckede MRebmann CFoken T. A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterisation of complex sites [J]. Agricultural and Forest Meteorology20041273/4: 175-188.

[81] Schmid H P. Experimental design for flux measurements: Matching scales of observations and fluxes [J]. Agricultural and Forest Meteorology1997872/3: 179-200. 

[82] Finnigan J. The footprint concept in complex terrain [J]. Agricultural and Forest Meteorology20041273/4: 117-129. 

[83] Foken TWichura B. Tools for quality assessment of surface-based flux measurements [J]. Agricultural and Forest Meteorology1996781/2: 83-105. 

[84] Sogachev ARannik UVesala T. Flux footprints over complex terrain covered by heterogeneous forest [J]. Agricultural and Forest Meteorology20041273/4: 143-158. 

[85] Kurbanmuradov ORannik USabelfeld Ket al. Evaluation of mean concentration and fluxes in turbulent flows by Lagrangian stochastic models [J]. Mathematics and Computers in Simulation2001546: 459-476.

[86] Schmid H P. Footprint modeling for vegetation atmosphere exchange studies: A review and perspective [J]. Agricultural and Forest Meteorology2002113: 159-183. 

[87] Vesala TRannik ULeclerc Met al. Flux and concentration footprints [J]. Agricultural and Forest Meteorology20041273/4:111-116. 

[88] Foken TLeclerc M Y. Methods and limitations in validation of footprint models [J]. Agricultural and Forest Meteorology20041273/4:223-234.

[89] Gockede MMarkkanen THasager C Bet al. Update of a footprint-based approach for the characterisation of complex measurement sites [J]. Boundary-Layer Meteorology20061183:635-655. 

[90] Rannik URaittila J. Turbulence statistics inside an over forest: Influence on footprint prediction [J]. Boundary-layer Meteorology2003109:137-166. 

[91] Rebmann CGockede MFoken Tet al. Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modeling [J]. Theoretical and Applied Climatology2005802/4: 121-141.

[92] Reithmaier L MGockede MMarkkanen Tet al. Use of remotely sensed land use classification for a better evaluation of micrometeorological flux measurement sites [J]. Theoretical and Applied Climatology2006844: 219-233.

[93] Kim JGuo QBaldocchi D Det al. Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolutionIKONOS images of vegetation cover[J]. Agricultural and Forest Meteorology20061363/4: 132-146. 

[94] House J IPrentice I CRamankutty Net al. Reconciling apparent inconsistencies in estimates of terrestrial CO2 sources and sinks [J]. Tellus Series B-Chemical and Physical Meteorology2003552: 345-363. 

[95] Xiao X MZhang Q YBraswell Bet al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data [J]. Remote Sensing of Environment2004912: 256-270.

[96] Zhao MRunning S WNemani R R. Sensitivity of Moderate Resolution Imaging Spectroradiometer MODIS terrestrial primary production to the accuracy of meteorological reanalyses [J]. Journal of Geophysical Research-Biogeosciences2006111:G01002. 

[97] Li Z QYu G RXiao X Met al. Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data [J]. Remote Sensing of Environment20071073: 510-519.

[98] Turner D PRitts W DCohen W Bet al. Scaling gross primary productionGPP over boreal and deciduous forest landscapes in support of MODIS GPP product validation [J]. Remote Sensing of Environment2003883: 256-270. 

[99] Xiao X MZhang Q YSaleska Set al. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest [J]. Remote Sensing of Environment2005941: 105-122. 

[100] Gebremichael MBarros A P. Evaluation of MODIS gross primary productivityGPP in tropical monsoon regions [J]. Remote Sensing of Environment20061002:150-166.

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