陆地生态系统植被生产力遥感模型研究进展

doi:10.11867/j.issn.1001-8166.2014.05.0541

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。

关键词: 植被生产力, 遥感, 光能利用率

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 CO₂concentration 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.

Key words: Gross primary production, Remote sensing, Light use efficiency.

中图分类号:

P935.1

Table 1 Comparison of potential light use efficiency and environmental stress factors for different light use efficiency models

模型	ε_g或ε_n	ε_max
Net Primary Production
CASA模型^{[ 17 ]}	ε_n=ε_max×f（T₁）×f（T₂）×f（SM）	0.389
Gross Primary Production
GLO-PEM^{[ 18 ]}	ε_n=ε_max×f（T）×f（SM）×f（VPD）	55.2α^a
	ε_n=ε_max×f（T）×f（SM）×f（VPD）	2.76^b
MODIS-GPP^{[ 36 ]}	ε_n=ε_max×f（T）×f（VPD）	0.604-1.259^c
VPM^{[ 22 , 37 ]}	ε_n=ε_max×f（T）×f（W）×f（P）	2.208,2.484^d
EC-LUE^{[ 32 ]}	ε_n=ε_max×Min（f（T）×f（W））	2.14^e
3-PG^{[ 38 ]}	ε_n=ε_max	1.8
C-Fix^{[ 19 ]}	ε_n=ε_max	1.1
注：NPP=ε_n×FPAR×PAR, GPP=ε_g×FPAR×PAR, 式中ε_n,ε_g分别为计算NPP和GPP的光能利用率, ε_max为潜在光能利用率。环境限制因子包括：温度f（T）、土壤湿度f（SM）、饱和水分亏缺f（VPD）、冠层水分状况f（W）和物候f（P）。^aC₃植物潜在光能利用率,α为光量子效率；^bC₄植物潜在光能利用率；^c为11种不同植被类型的潜在光能利用率；^d分别为针叶林和热带常绿阔叶林的潜在光能利用率；^e适用于所有植被类型

Table 1 Comparison of potential light use efficiency and environmental stress factors for different light use efficiency models

模型	ε_g或ε_n	ε_max
Net Primary Production
CASA模型^{[ 17 ]}	ε_n=ε_max×f（T₁）×f（T₂）×f（SM）	0.389
Gross Primary Production
GLO-PEM^{[ 18 ]}	ε_n=ε_max×f（T）×f（SM）×f（VPD）	55.2α^a
	ε_n=ε_max×f（T）×f（SM）×f（VPD）	2.76^b
MODIS-GPP^{[ 36 ]}	ε_n=ε_max×f（T）×f（VPD）	0.604-1.259^c
VPM^{[ 22 , 37 ]}	ε_n=ε_max×f（T）×f（W）×f（P）	2.208,2.484^d
EC-LUE^{[ 32 ]}	ε_n=ε_max×Min（f（T）×f（W））	2.14^e
3-PG^{[ 38 ]}	ε_n=ε_max	1.8
C-Fix^{[ 19 ]}	ε_n=ε_max	1.1
注：NPP=ε_n×FPAR×PAR, GPP=ε_g×FPAR×PAR, 式中ε_n,ε_g分别为计算NPP和GPP的光能利用率, ε_max为潜在光能利用率。环境限制因子包括：温度f（T）、土壤湿度f（SM）、饱和水分亏缺f（VPD）、冠层水分状况f（W）和物候f（P）。^aC₃植物潜在光能利用率,α为光量子效率；^bC₄植物潜在光能利用率；^c为11种不同植被类型的潜在光能利用率；^d分别为针叶林和热带常绿阔叶林的潜在光能利用率；^e适用于所有植被类型

图1 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°N,68.74°E；温带针叶林

Figure 1 FPAR calculated or adopted by several light use efficiency models at Howland station in US; latitude,45.20°N,longitude,68.74°E; temperate needleleaf forest

Table 2 Parameter values for various vegetation types of BIOME-BGC

参数值	C₃G	C₄G	ENF	DNF	EBF	DBF	SHR
空气动力导度/（mm/s）	0.01	0.01	1.0	1.0	1.0	1.0	0.1
最大气孔导度/（mm/s）	5.0	2.0	2.2	4.5	4.0	4.5	3.5
最大光合作用速率/（μmol/（m ^. s））	6.0	7.5	3.5	5.0	4.0	5.0	3.5
光合作用最适温度/（℃）	20	30	20	20	20	20	20

Table 2 Parameter values for various vegetation types of BIOME-BGC

参数值	C₃G	C₄G	ENF	DNF	EBF	DBF	SHR
空气动力导度/（mm/s）	0.01	0.01	1.0	1.0	1.0	1.0	0.1
最大气孔导度/（mm/s）	5.0	2.0	2.2	4.5	4.0	4.5	3.5
最大光合作用速率/（μmol/（m ^. s））	6.0	7.5	3.5	5.0	4.0	5.0	3.5
光合作用最适温度/（℃）	20	30	20	20	20	20	20

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Satellite-based Vegetation Production Models of Terrestrial Ecosystem: An Overview