地球科学进展  2014, Vol.29 Issue (5): 541-550  DOI:10.11867/j.issn.1001-8166.2014.05.0541
陆地生态系统植被生产力遥感模型研究进展
1.北京师范大学,地表过程与资源生态国家重点实验室,北京 100875; 2.中国科学院寒区旱区环境与工程研究所 冰冻圈科学国家重点实验室,甘肃 兰州 730000
Satellite-based Vegetation Production Models of Terrestrial Ecosystem: An Overview
1. State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China; 2. State Key Laboratory of Cryospheric Sciences,Cold and Arid Regions Environmental and Engineering Research Institute,The Chinese Academy of Sciences,Lanzhou,Gansu 730000,China

摘要

陆地生态系统植被生产力一直是全球变化领域内的研究热点,对其模拟的准确与否直接决定了后续碳循环要素的模拟精度,也关系到能否准确评估陆地生态系统对人类社会可持续发展的支持能力。遥感数据因其能够提供时空连续的植被变化信息,在区域植被生产力的模拟中扮演了不可替代的角色。目前遥感模型可以分为统计模型和过程模型2类。前者主要基于植被指数等与观测值的统计关系,从最初的线性关系发展到利用回归树等多变量的统计模型。后者则是基于光能利用率原理,借助于遥感数据的时空连续性实现对区域和全球植被生产力的准确评估。然而,这些模型在计算植物冠层吸收的光合有效辐射比例、环境对最大光能利用率的限制等诸多方面存在显著的差异,对于一些关键的生态系统过程描述不完善,总体而言模拟能力仍然有待提高。此外,遥感数据也被广泛地应用于动态植被模型的发展和应用中,为模拟提供植被类型、叶面积指数等关键的输入数据。后续的研究应该进一步改进模型公式,发展集合预估算法,并应考虑由于输入数据和参数的不确定性而导致的区域模拟误差,以提高对区域植被生产力的模拟精度。

Abstract

Vegetation,as the principal component of terrestrial ecosystem,plays an important role in sustaining global substance and energy cycle,adjusting carbon balance and alleviating the rise of atmospheric CO2 concentration and global climate change. Vegetation production of terrestrial ecosystem has been one of the major subjects for the research on global change. The satellite-based model of vegetation productivity has undergone several stages of development,including the initial simple statistical model,the later process model based on light use efficiency principle. Based on remote sensing vegetation data with spatially and temporally continuous distribution,statistical model is crucial in estimating vegetation productivity on the regional and global scale. Statistical model can be classified into two categories: one is direct establishment of the correlation between vegetation index and vegetation productivity,based on which regional estimation is possible; the other is the establishment of regression parameter vector for regional applications,which is realized through the integrated utilization of vegetation indices and other environmental factors and using regression tree,neural network and other complex statistical methods. Light use efficiency model is the major approach to estimating vegetation productivity based on remote sensing data. However,there are large differences on the calculations of the fraction of absorbed photosynthetically active radiation,environmental stress factors,and the model performance also need improve. Future studies should continue to improve model ability,develop multiple model ensemble algorithms and provide simulation uncertainties.

基金资助

国家自然科学基金优秀青年基金项目“碳循环遥感模型”(编号:41322005); 国家高技术研究发展计划项目“基于碳卫星的遥感定量检测应用技术研究”(2013AA122003)资助

引用本文

[中文]
袁文平, 蔡文文, 刘丹, 董文杰. 陆地生态系统植被生产力遥感模型研究进展[J]. 地球科学进展, 2014, 29(5): 541-550.
[英文]
Wenping Yuan, Wenwen Cai, Dan Liu, Wenjie Dong. Satellite-based Vegetation Production Models of Terrestrial Ecosystem: An Overview[J]. Advance in Earth Science, 2014, 29(5): 541-550.
使用本文
PACS
本文作者
阅读笔记
在左边选中内容后,点击→加入笔记。笔记内容将复制到下面文本框中,点击保存按钮可保存在个人文献中心中
              
几种光能利用率模型计算或采用的冠层光合有效辐射吸收比例FPAR
以美国Howland站点为例,经纬度:45.20°
FPAR calculated or adopted by several light use efficiency models
at Howland station in US
[1]
Lieth H. Historical survey of primary productivity research[M]∥Lieth H,Whittaker R H, eds. Primary Productivity of the Biosphere. New York: Springer Berlin Heidelberg, 1975: 7-16.
[2]
Fang Jingyun,Ke Jinhu,Tang Zhiyao,et al. Implications and estimations of four terrestrial productivity parameters[J]. Chinese Journal of Plant Ecology, 2001, 25(4): 414-419.[方精云,柯金虎,唐志尧,等. 生物生产力的“4P”概念、估算及其相互关系[J]. 植物生态学报,2001, 25(4): 414-419.]
[3]
Vitousek P M,Mooney H A,Lubchenco J,et al. Human domination of Earth’s ecosystems[J]. Science,1997,277(5 325):494-499.
[4]
Cramer W,Kicklighter D W,Bondeau A,et al. Comparing global models of terrestrial Net Primary Productivity (NPP): Overview and key results[J]. Global Change Biology,1999,5(S1):1-15.
[5]
Keenan T,Ian B,Alan B,et al. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange[J]. Global Change Biology,2012,18(6):1 971-1 987.
[6]
Chen Jiquan, Yang Shuying. Ecological Methods of Terrestrial Ecosystems[M]. Beijing: Higher Education Press, 2014.[陈吉泉,阳树英. 陆地生态学研究方法[M]. 北京:高等教育出版社,2014.]
[7]
Baldocchi D D, Falge E, Gu L, et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor and energy flux densities[J]. Bulletin of the American Meteorological Society, 2001, 82: 2 415-2 435.
[8]
Baldocchi D D. Breathing of the terrestrial biosphere: Lessons learned from a global network of carbon dioxide flux measurement systems[J]. Australian Journal of Botany,2008,56(1):1-26.
[9]
Goward S A,Tucker C J,Dye D G. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer[J]. Vegetatio,1985,64(1):3-14.
[10]
Paruelo J M,Epstein H E,Lauenroth W K, et al. ANPP estimates from NDVI for the central grassland region of the United States[J]. Ecology,1997,78(3):953-958.
[11]
Paruelo J M,Oesterheld M,Di Bella C M,et al. Estimation of primary production of sub-humid rangelands from remote sensing data[J]. Applied Vegetation Science,2000,3(2):189-195.
[12]
Box E O,Holben B, Kalb V. Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux[J]. Vegetation,1989,80(2):71-89.
[13]
Huete A R,Jackson R D. Soil and atmosphere influences on the spectra of partial canopies[J]. Remote Sensing of Environment,1988,25(1):89-105.
[14]
Gamon J A,Field C B,Goulden M L,et al. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types[J]. Ecological Applications,1995,5(1):28-41.
[15]
Beer C,Reichstein M,Tomelleri E,et al. Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate[J]. Science,2010,329(5 993):834-838.
[16]
Zhang L,Wylie B,Loveland T,et al. Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands[J]. Remote Sensing of Environment,2007,106(2):173-189.
[17]
Potter C S,Randerson J T,Field C B,et al. Terrestrial ecosystem production: A process model based on global satellite and surface data[J]. Global Biogeochemical Cycles,1993,7(4):811-841.
[18]
Prince S D,Goward S N. Global primary production: A remote sensing approach[J]. Journal of Biogeography,1995,22(45):815-835.
[19]
Veroustraete F,Sabbe H,Eerens H. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data[J]. Remote Sensing of Environment,2002,83(3):376-399.
[20]
Turner D P,Ritts W D,Styles J M,et al. A diagnostic carbon flux model to monitor the effects of disturbance and interannual variation in climate on regional NEP[J]. Tellus,2006,58(5):476-490.
[21]
King D A,Turner D P, Ritts W D. Parameterization of a diagnostic carbon cycle model for continental scale application[J]. Remote Sensing of Environment,2011,115(7):1 653-1 664.
[22]
Xiao X,Hollinger D,Aber J,et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest[J]. Remote Sensing of Environment,2004,89(4):519-534.
[23]
Mahadevan P,Wofsy S C,Matross D M,et al. A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model(VPRM)[J]. Global Biogeochemical Cycles,2008,22(2):1-17.
[24]
He M Z,Ju W M,Zhou Y L,et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity[J]. Agricultural and Forest Meteorology,2013,173:28-39.
[25]
Yuan Wenping,Liu Shuguang,Zhou Guangsheng, et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[J]. Agricultural and Forest Meteorology,2007,143(34):189-207.
[26]
Field C B,Randerson J T,Malmstrm C M. Global net primary production: Combining ecology and remote sensing[J]. Remote Sensing of Environment,1995,51(1):74-88.
[27]
Running S W,Nemani R R,Heinsch F A,et al. A continuous satellite-derived measure of global terrestrial primary production[J]. Bioscience,2004,54(6):547-560.
[28]
Wang Lijuan,Niu Zheng,Kuang Da. An analysis of the terrestrial NPP from 2002 to 2006 in China based on MODIS data[J]. Remote Sensing for Land & Recourses,2010,22(4):113-116.[王李娟,牛铮,旷达. 基于MODIS数据的2002—2006年中国陆地NPP分析[J]. 国土资源遥感,2010,22(4):113-116.]
[29]
Guo Xiaoyin,He Yong,Shen Yongping,et al. Analysis of the terrestrial NPP based on the MODIS in the source regions of Yangtze and Yellow Rivers from 2000 to 2004[J]. Journal of Glaciology and Geocryology,2006,28(4):512-518.[郭晓寅,何勇,沈永平,等. 基于MODIS资料的2000—2004年江河源区陆地植被净初级生产力分析[J]. 冰川冻土,2006,28(4):512-518.]
[30]
Zhu Wenquan,Pan Yaozhong,He Hao,et al. Simulation of maximum light use efficiency for some typical vegetation types in China[J]. Chinese Science Bulletin,2006,51(4):457-463.[朱文泉,潘耀忠,何浩,等.中国典型植被类型最大光能利用率模拟[J]. 科学通报,2006,51(4):457-463.]
[31]
Wang X F,Ma M G,Li X,et al. Validation of MODIS GPP product at 10 flux sites in northern China[J]. International Journal of Remote Sensing,2013,34(2):587-599.
[32]
Yuan W P,Liu S G,Zhou G S,et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[J]. Agricultural and Forest Meteorology,2007,143(34):189-207.
[33]
Yuan W P,Liu S G,Yu G R,et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data[J]. Remote Sensing of Environment,2010,114(7):1 416-1 431.
[34]
Li X L,Liang S,Yu G R,et al. Estimation of gross primary production over the terrestrial ecosystems in China[J]. Ecological Modeling,2013,261262:80-92.
[35]
Piao S L,Luyssaert S,Ciais P,et al. Forest annual carbon cost: A global-scale analysis of autotrophic respiration[J]. Ecology,2010,91(3):652-657.
[36]
Running S W,Nemani R,Glassy J M,et al. MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) Product (MOD17) Algorithm Theoretical Basis Document[Z].1999.
[37]
Xiao X,Zhang Q,Hollinger D,et al. Modeling seasonal dynamics of gross primary production of evergreen needleleaf forest using MODIS images and climate data[J]. Ecological Applications,2005,15(3):954-969.
[38]
Landsberg J J, Waring R H. A generalised model of forest productivity using simplified concepts of radiation-use efficiency,carbon balance and partitioning[J]. Forest Ecology and Management,1997,95(3):209-228.
[39]
Yuan W P,Cai W,Xia J Z,et al. Global Comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database[J]. Agricultural and Forest Meteorology,2014,192193:108-120.
[40]
Gu L H,Baldocchi D D,Verma S B,et al. Advantages of diffuse radiation for terrestrial ecosystem productivity[J]. Journal of Geophysical Research: Atmospheres,2002,107(D6):ACL 2-1-ACL 2-23.
[41]
Gu L H,Baldocchi D D,Wofsy S C,et al. Response of a deciduous forest to the Mount Pinatubo eruption: Enhanced photosynthesis[J]. Science,2003,299(5 615):2 035-2 038.
[42]
Alton P B,North P R, Los S O. The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes[J]. Global Change Biology,2007,13(4):776-787.
[43]
Urban O,Janou D,Acosta M,et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparsion of the response in direct vs. diffuse solar radiation[J]. Global Change Biology,2007,13(1):157-168.
[44]
Hollinger D Y,Kelliher F M,Byers J N,et al. Carbon dioxide exchange between an undisturbed old-growth temperate forest and the atmosphere[J]. Ecology,1994,75(1):134-150.
[45]
Sakai R K,Fitzjarrald D R,Moore K E,et al. How do forest surface fluxes depend on fuctuating light level?[C]∥Conference on Agricultural and Forest Meteorology with Symposium on Fire and Forest Meteorology,1996,22: 90-93.
[46]
Matsuda R,Ohashi-Kaneko K,Fujiwara K,et al. Photosynthetic characteristics of rice leaves grown under red light with or without supplemental blue light[J]. Plant and Cell Physiology,2004,45(12):1 870-1 874.
[47]
Bonan G B,Levis S,Sitch S,et al. A dynamic global vegetation model for use with climate models: Concepts and description of simulated vegetation dynamics[J]. Global Change Biology,2003,9(11):1 543-1 566.
[48]
Zhuang Q,McGuire A D,Melillo J M,et al. Carbon cycling in extratropical terrestrial ecosystems of the Northern Hemisphere during the 20th Century: A modeling analysis of the influences of soil thermal dynamics[J]. Tellus, 2003, 55B: 751-776.
[49]
Jia Kun,Yao Yunjun,Wei Xiangqin, et al. A review on fractional vegetation cover estimation using remote sensing[J]. Advances in Earth Science,2013,28(7):774-782.[贾坤,姚云军,魏香琴,等. 植被覆盖度遥感估算研究进展[J]. 地球科学进展,2013,28(7):774-782.]
[50]
Wang Zhihui,Liu Liangyun. Monitoring on land cover pattern and crops structure of oasis irrigation area of middlereaches in Heihe River Basin using remote sensing data[J]. Advances in Earth Science,2013,28(8):948-956.[王志慧,刘良云. 黑河中游绿洲灌溉区土地覆盖与种植结构空间格局遥感监测[J]. 地球科学进展,2013,28(8):948-956.]
[51]
Foley J A,Prentice C,Ramankutty N, et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics[J]. Global Biogeochemical Cycles,1996,10(4):603-628.
[52]
Ryu Y,Baldocchi D D,Kobayashi H, et al. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1km to global scales[J]. Global Biogeochemical Cycles,2011,25,doi:10.10292011GB004053.
[53]
Michaelides S C,Tymvios F S,Michaelidou T. Spatial and temporal characteristics of the yearly rainfall frequency distribution in Cyprus[J]. Atmospheric Research,2009,94(4):606-615.
[54]
Cai W,Yuan W P,Liang S,et al. Improved estimations of gross primary production using satellite-derived photosynthetically active radiation[J]. Journal of Geophysical Research: Biogeosciences,2014,119,doi:10.10022013JG002456.
数据正在加载中...