全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
草业学报  2012 

基于MODIS算法的藏北高寒草甸的光能利用效率模拟

, PP. 239-247

Keywords: 光能利用效率,高寒草甸,MODIS,藏北高原

Full-Text   Cite this paper   Add to My Lib

Abstract:

光能利用效率(lightuseefficiency,LUE)是指初级生产力与植被冠层所吸收的光合有效辐射(absorbedphotosyntheticallyactiveradiation,APAR)之比,它反映了植被利用光能的能力。定量化生产力的时空变化是定量化全球碳循环的重要研究内容,而LUE作为光能生产力模型中的一个重要参数,是定量化生产力时空变化的基础。因此,定量化全球植被的LUE是定量化全球碳循环的重要组成部分。基于MODIS光能利用效率算法,本研究模拟了2004-2005年藏北高寒草甸生态系统的光能利用效率(LUEMODIS),并用观测的光能利用效率(LUEEC)对模型进行了验证。在MODIS算法中,日最低气温(Tamin)和饱和水汽压亏缺(VPD)分别被用来计算温度胁迫因子(Tscalar)和水分胁迫因子(Wscalar)。相关分析和多重逐步回归分析结果表明,相对于Wscalar,Tscalar更能够解释观测的LUE的季节变化。2004和2005年的模拟值分别高估了约14.97%和16.57%的观测值,但配对T检验显示模拟值和观测值差异不显著,即基于MODIS的LUE算法在模拟藏北高寒草甸LUE方面具有较高的精度。相关分析表明,观测的LUE与Tamin的相关性好于观测的LUE与平均气温的相关性,这表明在反应藏北高寒草甸生态系统LUE的季节变异方面,Tamin优于平均气温。总之,基于MODIS算法的LUE模型能够比较准确地定量化藏北高寒草甸生态系统的LUE。

References

[1]  Prince S D, Goward S N. Global primary production: a remote sensing approach. Journal of Biogeography, 1995, 22: 815-835.
[2]  Potter C S, Randerson J T, Field C B, et al. Terrestrial ecosystem production: a process model-based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811-841.
[3]  王莺, 夏文韬, 梁天刚, 等. 基于MODIS植被指数的甘南草地净初级生产力时空变化研究. 草业学报, 2010, 19(1): 201-210. 浏览
[4]  公延明, 胡玉昆, 阿德力麦地, 等. 高寒草原对气候生产力模型的适用分析. 草业学报, 2010, 19(2): 7-13. 浏览
[5]  王雯玥, 韩清芳, 宗毓峥, 等. 不同叶型紫花苜蓿不同茬次光合效率的差异. 草业科学, 2010, 27(5): 50-56.
[6]  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. Agricultural and Forest Meteorology, 2007, 143: 189-207.
[7]  Wu C Y, Niu Z, Tang Q, et al. Remote estimation of gross primary production in wheat using chlorophyII-related vegetation indices. Agricultural and Forest Meteorology, 2009, 149: 1015-1021.
[8]  Coops N C, Jassal R S, Leuning R, et al. Incorporation of a soil water modifier into MODIS predictions of temperate Douglas-fir gross primary productivity: Initial model development. Agricultural and Forest Meteorology, 2007, 147: 99-109.
[9]  Running S W, Nemani R R, Heinsch F A, et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 2004, 54: 547-560.
[10]  Cook B D, Bolstad P V, Naesset E, et al. Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. Remote Sensing of Environment, 2009, 113: 2366-2379.
[11]  Heinsch F A, Zhao M S, Running S W, et al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44: 1908-1925.
[12]  Zhang Y Q, Yu Q, Jiang J, et al. Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau. Global Change Biology, 2008, 14: 757-767.
[13]  Zhao M S, Heinsch F A, Nemani R R. Improvements of the MODIS terrestrial gross and primary production global data set. Remote Sensing of Environment, 2005, 95: 164-176.
[14]  Turner D P, Ritts W D, Cohen W B, et al. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment, 2006, (3-4): 282-292.
[15]  Xu L L, Zhang X Z, Shi P L, et al. Establishment of apparent quantum yield and maximum ecosystem assimilation on Tibetan Plateau alpine meadow ecosystem. Sciences in China Series D, 2005, 48: 141-147.
[16]  Ni J. Carbon storage in grasslands of China. Journal of Arid Environment, 2002, 50: 205-218.
[17]  李东, 黄耀, 吴琴, 等. 青藏高原高寒草甸生态系统土壤有机碳动态模拟研究. 草业学报, 2010, 19(2): 160-168. 浏览
[18]  周刊社, 杜军, 袁雷, 等. 西藏怒江流域高寒草甸气候生产潜力对气候变化的响应. 草业学报, 2010, 19(5): 17-24. 浏览
[19]  仁青吉, 武高林, 任国华. 放牧强度对青藏高原东部高寒草甸植物群落特征的影响. 草业学报, 2009, 18(5): 256-261. 浏览
[20]  付刚, 沈振西, 张宪洲, 等. 基于MODIS影像的藏北高寒草甸的蒸散模拟. 草业学报, 2010, 19(5): 103-112. 浏览
[21]  Falge E, Baldocchi D, Olson R, et al. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 2001, 107: 43-69.
[22]  Fu Y, Zheng Z, Yu G, et al. Environmental influences on carbon dioxide fluxes over three grassland ecosystem in China. Biogeosciences, 2009, 6: 2879-2893.
[23]  Yu G R, Zhang L M, Sun X M, et al. Environmental controls over carbon exchange of three forest ecosystems in eastern China. Global Change Biology, 2008, 14: 2555-2571.
[24]  Ruimy A, Kergoat L, Bondeau A, et al. Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency. Global Change Biology, 1999, 5: 56-64.
[25]  Xu L L, Zhang X Z, Shi P L, et al. Modeling the maximum apparent quantum use efficiency of alpine meadow ecosystem on Tibetan Plateau. Ecological Modelling, 2007, 208: 129-134.
[26]  Xiao X M, Zhang Q Y, Braswell B, et al. Modeling gross primary production of temperature deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 2004, 91: 256-270.
[27]  Almeida A C, Landsberg J J. Evaluating methods of estimating global radiation and vapor pressure deficit using a dense network of automatic weather stations in coastal Brazil. Agricultural and Forest Meteorology, 2003, 118: 237-250.
[28]  同小娟, 李俊, 王玲. 农田光能利用效率研究进展. 生态学杂志, 2008, 27(6): 1021-1028.
[29]  同小娟, 李俊, 于强. 农田生态系统光能利用效率及其影响因子分析. 自然资源学报, 2009, 24(8): 1393-1401.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133