VPM模型最大光能利用效率参数优化对不同森林生态系统GPP模拟的影响

doi:10.11707/j.1001-7488.LYKX20240355

Abstract

Abstract:

Objective: To analyze whether there are differences in the maximum light use efficiency (ε₀ ), a key parameter in the vegetation photosynthesis model (VPM), in forest ecosystems of different climatic zones and the main reasons for such differences, and to select a parameterization scheme for the maximum light use efficiency that has general applicability. In order to deepen the knowledge of uncertainty in the process of estimating vegetation productivity in forest ecosystems and to provide a reference for improving model simulation accuracy and reducing the uncertainty of model parameters. Method: The maximum light use efficiency (ε₀ ) was estimated using the four selected parameterization schemes, BPLUT look-up table method, Michealis-Menten light response curve equation fitting, maxmiun enhanced vegetation index (EVImax) of the growing season exponential fitting, and Mointeith equation estimation method. Simulation of gross primary productivity (GPP) of four forest ecosystems in China based on the estimation results of the four parameterization schemes, comparison with eddy covariance observed GPP, and evaluation of VPM simulation results by combining various model evaluation indexes (coefficient of determination R², root mean square error RMSE, the Willmott’s index of agreement d and mean relative error MRE). Result: The ε₀ values were （0.65±0.14g）, （0.47±0.10）, （0.44±0.09), and (0.69±0.12) g·mol, respectively for the Changbaishan, Qianyanzhou, Dinghushan, and Xishuangbanna. On the seasonal scale, compared with the GPP based on observation, root mean square error(RMSE) of GPP simulated by the optimal parameterization scheme for ε₀ is reduced by 55.1%(Changbaishan), 38.1%(Qianyanzhou), 48.6%(Dinghushan), and 34.3%(Xishuangbanna), respectively, than that simulated by the least applicable parameterization scheme. On the inter-annual scale, the mean relative errors (MRE) of the GPP simulated by the optimal parameterization scheme were ?7.9%, ?24.3%, ?7.4%, and ?3.0% respectively at Changbaishan, Qianyanzhou, Dinghushan, and Xishuangbanna, which were lower than that (Changbaishan: 35.8%; Qianyanzhou: ?53.4%; Dinghushan: 29.8%; Xishuangbanna: 25.4%) simulated by the least applicable parameterization scheme. Conclusion: The ε₀ values of different parameterization schemes at the same site vary greatly, and there are differences in the ε₀ values between different sites under the same parameterization scheme. The main reasons for such differences are related to the structural properties of the parameterization schemes and the differences in hydrothermal conditions in different regions. Mointeith equation estimation method was the optimal parameterization scheme for ε₀ in the Changbaishan, Qianyanzhou, and Dinghushan, and Maxmiun enhanced vegetation index (EVImax) of the growing season exponential fitting is the most applicable in the Xishuangbanna.

Key words: maximum light use efficiency, parameterization, VPM model, gross primary productivity

CLC Number:

S718.55

Fuyu Yang,Mi Zhang,Wei Xiao,Jie Shi. Impacts of Optimizing Maximum Light Use Efficiency Parameter in VPM on GPP Simulation in Different Forest Ecosystems[J]. Scientia Silvae Sinicae, 2025, 61(3): 86-99.

Figures/Tables 9

Table 1

Table 2

Table 3

Table 4

Related information of literature research sites"

站点所在地 Sites Location	森林类型 Forest type	研究时段 Start-stop year	最大光能利用率 Maximum light use efficiency /（g·mol^?1）	生长季增强型植被指数最大值 Maxmium enhanced vegetation index of the growing season	来源 Source
中国 China	常绿针叶林Evergreen needleleaf forest	2008	0.57	0.46	Wang et al., 2010a
中国 China	落叶阔叶林 Deciduous broad-leaved forest	2008	0.40	0.52	Wang et al., 2010a
中国 China	落叶阔叶林 Deciduous broad-leaved forest	2008	0.65	0.52	Wang et al., 2010a
中国 China	混交林 Mixed forest	2003—2005	0.73	0.62	Wu et al., 2009
中国 China	常绿阔叶林 Evergreen broad-leaved forest	2003	0.36	0.49	张雷明等, 2006
中国 China	常绿针叶林Evergreen needleleaf forest	2003	0.38	0.53	刘允芬等, 2004
中国 China	常绿阔叶林 Evergreen broad-leaved forest	2003	0.70	0.58	张雷明等, 2006
美国America	落叶阔叶林 Deciduous broad-leaved forest	2003—2006	0.53	0.70	Wu et al., 2010
美国America	常绿针叶林Evergreen needleleaf forest	1998—2002	0.48	0.56	Xiao et al., 2004a
巴西 Brazil	常绿阔叶林 Evergreen broad-leaved forest	2001—2003	0.54	0.68	Xiao et al., 2005
美国America	落叶阔叶林 Deciduous broad-leaved forest	2004—2005	0.49	0.72	Cheng et al., 2014
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2001—2005	0.46	0.64	Bonan et al., 2011
加拿大 Canada	混交林 Mixed forest	2003—2005	0.49	0.63	Migliavacca et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2001—2005	0.30	0.35	Horn et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2001—2006	0.25	0.40	Bonan et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2003—2005	0.40	0.38	Bonan et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2004—2005	0.26	0.40	Bonan et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2003—2005	0.57	0.52	Groenendijk et al., 2009
加拿大 Canada	混交林 Mixed forest	2003—2005	0.38	0.42	Piao et al., 2008
美国America	落叶阔叶林 Deciduous broad-leaved forest	2004—2005	0.59	0.72	Horn et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2000—2006	0.36	0.38	Horn et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2005—2006	0.50	0.32	Horn et al., 2011
美国America	混交林 Mixed forest	2000—2006	0.73	0.69	Zhang et al., 2011
美国America	混交林 Mixed forest	2000—2004	0.57	0.59	Zhang et al., 2011
美国America	混交林 Mixed forest	2000—2004	0.52	0.57	Hollinger et al., 2010
美国America	常绿阔叶林 Evergreen broad-leaved forest	2000—2006	0.52	0.48	Lasslop et al., 2010
美国America	混交林 Mixed forest	2001—2005	0.40	0.61	Hashimoto et al., 2008
美国America	落叶阔叶林 Deciduous broad-leaved forest	2002—2005	0.63	0.69	Lasslop et al., 2010
美国America	常绿针叶林Evergreen needleleaf forest	2004—2005	0.37	0.25	Misson et al., 2007
美国America	落叶阔叶林 Deciduous broad-leaved forest	2000—2005	0.61	0.76	Dragoni et al., 2011
美国America	落叶阔叶林 Deciduous broad-leaved forest	2004—2006	0.70	0.72	Horn et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2000—2003	0.31	0.37	Horn et al., 2011
美国America	落叶阔叶林 Deciduous broad-leaved forest	2004—2005	0.79	0.69	Sun et al., 2011
美国America	常绿阔叶林 Evergreen broad-leaved forest	2000—2004	0.38	0.51	Horn et al., 2011
美国America	混交林 Mixed forest	2002—2006	0.55	0.52	Horn et al., 2011
美国America	落叶阔叶林 Deciduous broad-leaved forest	2000—2003	0.64	0.69	Horn et al., 2011
美国America	落叶阔叶林 Deciduous broad-leaved forest	2000—2006	0.71	0.74	Horn et al., 2011
美国America	混交林 Mixed forest	2002—2005	0.59	0.56	Saito et al., 2009
美国America	常绿针叶林Evergreen needleleaf forest	2000—2006	0.38	0.48	Horn et al., 2011
美国America	混交林 Mixed forest	2001—2005	0.60	0.64	Horn et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2000—2005	0.29	0.46	Bonan et al., 2011
美国America	落叶阔叶林 Deciduous broad-leaved forest	2002—2002	0.66	0.69	Bonan et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2000—2000	0.30	0.30	Heinsch et al., 2006
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2003—2005	0.53	0.51	Coursolle et al., 2012
美国America	常绿针叶林Evergreen needleleaf forest	2004—2005	0.53	0.26	Yi et al., 2010
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2000—2005	0.60	0.51	Hilker et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2001—2005	0.28	0.40	Goulden et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2001—2005	0.32	0.44	Goulden et al., 2011
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2000—2005	0.26	0.28	Bonan et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2003—2005	0.50	0.38	Horn et al., 2011
美国America	常绿针叶林Evergreen needleleaf forest	2003—2003	0.36	0.38	Wang et al., 2010b
加拿大 Canada	常绿针叶林Evergreen needleleaf forest	2000—2005	0.29	0.33	Bonan et al., 2011

Table 4

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

References 0

	常晓晴, 邢艳秋, 王馨慧, 等. 3PG碳生产模型在长白山阔叶红松林总初级生产力估算中的应用. 应用生态学报, 2019, 30 (5): 1599- 1607.
	Chang X Q, Xing Y Q, Wang X H, et al. Application of 3PG carbon production model in the gross primary productivity estimation of broadleaved Korean pine forest in Changbai Mountain. Chinese Journal of Applied Ecology, 2019, 30 (5): 1599- 1607.
	戴晓琴, 王辉民, 徐明洁, 等. 2003–2010年千烟洲人工针叶林碳水通量观测数据集. 中国科学数据, 2021, 6 (1): 7- 15.
	Dai X Q, Wang H M, Xu M J, et al. An observation dataset of carbon and water fluxes of artificial coniferous forests in Qianyanzhou (2003–2010). Science Data Bank, 2021, 6 (1): 7- 15.
	房秋兰, 沙丽清. 西双版纳热带季节雨林与橡胶林土壤呼吸. 植物生态学报, 2006, 30 (1): 97- 103. doi: 10.3321/j.issn:1005-264X.2006.01.014
	Fang Q L, Sha L Q. Soil respiration in a tropical seasonal rainforest and rubber plantation in Xishuangbanna Yunnan SW China. Journal of Plant Ecology, 2006, 30 (1): 97- 103. doi: 10.3321/j.issn:1005-264X.2006.01.014
	高德新, 王　帅, 李　琰, 等. 植被光能利用率: 模型及其不确定性. 生态学报, 2021, 41 (14): 5507- 5516.
	Gao D X, Wang S, Li Y, et al. Light use efficiency of vegetation: model and uncertainty. Acta Ecologica Sinica, 2021, 41 (14): 5507- 5516.
	葛宏立, 张丽景. 利用MODIS数据估测毛竹林总初级生产力. 浙江农林大学学报, 2014, 31 (2): 178- 184.
	Ge H L, Zhang L J. Gross primary production in Phyllostachys edulis based on MODIS. Journal of Zhejiang A&F University, 2014, 31 (2): 178- 184.
	贾文晓, 刘　敏, 佘倩楠, 等. 基于FLUXNET观测数据与VPM模型的森林生态系统光合作用关键参数优化及验证. 应用生态学报, 2016, 27 (4): 1095- 1102.
	Jia W X, Liu M, She Q N, et al. Optimization and evaluation of key photosynthesis parameters in forest ecosystems based on FLUXNET data and VPM model. Chinese Journal of Applied Ecology, 2016, 27 (4): 1095- 1102.
	李登秋, 张春华, 居为民, 等. 江西省森林净初级生产力动态变化特征及其驱动因子分析. 植物生态学报, 2016, 40 (7): 45- 59.
	Li D Q, Zhang C H, Ju W M, et al. Forest net primary productivity dynamics and driving forces in Jiangxi Province, China. Chinese Journal of Plant Ecology, 2016, 40 (7): 45- 59.
	李跃林, 闫俊华, 孟　泽, 等. 2003–2010年鼎湖山针阔叶混交林碳水通量观测数据集. 中国科学数据, 2021, 6 (1): 16- 26.
	Li Y L, Yan J H, Meng Z, et al. An observation dataset of carbon and water fluxes in a mixed coniferous broad-leaved forest at Dinghushan, Southern China (2003 – 2010). Science Data Bank, 2021, 6 (1): 16- 26.
	刘允芬, 宋　霞, 孙晓敏, 等. 千烟洲人工针叶林CO₂通量季节变化及其环境因子的影响. 中国科学(D辑: 地球科学), 2004, 34 (S2): 109- 117.
	Liu Y F, Song X, Sun X M, et al. Seasonal variation of CO₂ flux in artificial coniferous forest in Qianyan⁃ zhou and its environmental factors. Science in China (Series D: Earth Sciences), 2004, 34 (S2): 109- 117.
	起德花, 张一平, 宋清海, 等. 2003–2010年西双版纳热带季节雨林碳水和能量通量观测数据集. 中国科学数据, 2021, 6 (1): 37- 49.
	Qi D H, Zhang Y P, Song Q H, et al. A dataset of carbon, water and energy fluxes observed in Xishuangbanna tropical seasonal rain forest from 2003 to 2010. Science Data Bank, 2021, 6 (1): 37- 49.
	石旭霞, 宋沼鹏, 侯继华, 等. 中国东部森林最大总初级生产力的时空分布特征及其影响因子. 生态学杂志, 2019, 38 (7): 1949- 1961.
	Shi X X, Song Z P, Hou J H, et al. Spatiotemporal patterns of the maximum primary productivity and driving factors in the eastern China’s forests. Chinese Journal of Ecology, 2019, 38 (7): 1949- 1961.
	魏识广, 叶万辉, 练琚愉, 等. 南亚热带常绿阔叶林群落及其卫星样地物种多样性特征. 生态学报, 2022, 42 (11): 4515- 4523.
	Wei S G, Ye W H, Lian J Y, et al. Species diversity characteristics of lower subtropical evergreen broad⁃leaved forest community and its satellite. Acta Ecologica Sinica, 2022, 42 (11): 4515- 4523.
	吴家兵, 关德新, 王安志, 等. 2003–2010年长白山阔叶红松林碳水通量观测数据集. 中国科学数据, 2021, 6 (1): 27- 36.
	Wu J B, Guan D X, Wang A Z, et al. A dataset of carbon and water flux observation in a broad-leaved red pine forest in Changbai Mountain (2003–2010). Science Data Bank, 2021, 6 (1): 27- 36.
	张雷明, 于贵瑞, 孙晓敏, 等. 中国东部森林样带典型生态系统碳收支的季节变化. 中国科学(D辑: 地球科学), 2006, 36 (S1): 45- 59.
	Zhang L M, Yu G R, Sun X M, et al. Seasonal variation of carbon budget in a typical ecosystem of forest strips in eastern China. Science in China (Series D: Earth Sciences), 2006, 36 (S1): 45- 59.
	周存宇, 周国逸, 张德强, 等. 鼎湖山森林地表CO₂通量及其影响因子的研究. 中国科学(D辑: 地球科学), 2004, 34 (S2): 175- 182.
	Zhou C Y, Zhou G Y, Zhang D Q, et al. Research on forest floor CO₂ flux and its influencing factors in Dinghu Mountain. Science in China (Series D: Earth Sciences), 2004, 34 (S2): 175- 182.
	Balzarolo M, Valdameri N, Fu Y H, et al. Different determinants of radiation use efficiency in cold and temperate forests. Global Ecology and Biogeography, 2019, 28 (11): 1649- 1667. doi: 10.1111/geb.12985
	Beer C, Reichstein M, Tomelleri E, et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 2010, 329 (5993): 834- 838. doi: 10.1126/science.1184984
	Bonan G B, Lawrence P J, Oleson K W, et al. Improving canopy processes in the community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. Journal of Geophysical Research, 2011, 116 (2): G02014.
	Cheng Y B, Middleton E M, Hilker T, et al. Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest. Measurement Science and Technology, 2009, 20 (9): 095107. doi: 10.1088/0957-0233/20/9/095107
	Cheng Y B, Zhang Q Y, Lyapustin A I, et al. Impacts of light use efficiency and fPAR parameterization on gross primary production modeling. Agricultural and Forest Meteorology, 2014, 189, 187- 197.
	Coursolle C, Margolis, H A, Giasson M A, et al. Influence of stand age on the magnitude and seasonality of carbon fluxes in Canadian forests. Agricultural and Forest Meteorology, 2012, 165, 136- 148. doi: 10.1016/j.agrformet.2012.06.011
	Dragoni D, Schmid H P, Wayson C A, et al. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Global Change Biology, 2011, 17 (2): 886- 897. doi: 10.1111/j.1365-2486.2010.02281.x
	Feng S Q, Tang B H, Chen G K, et al. Estimating the gross primary productivity based on VPM correction model for Xishuangbanna tropical seasonal rainforest. International Journal of Remote Sensing, 2023, 45 (2): 1- 20.
	Goulden M L, Daube B C, Fan S M, et al. Physiological responses of a black spruce forest to weather. Journal of Geophysical Research Atmospheres, 1997, 102 (24): 28987- 28996.
	Goulden M L, Mcmillan A M S, Winston G C, et al. Patterns of NPP, GPP, respiration, and NEP during boreal forest succession. Global Change Biology, 2011, 17 (2): 855- 871. doi: 10.1111/j.1365-2486.2010.02274.x
	Groenendijk M, Molen M K, Dolman A J. Seasonal variation in ecosystem parameters derived from FLUXNET data. Biogeosciences Discuss, 2009, 6 (2): 2863- 2912.
	Hashimoto H, Dungan J, White M, et al. Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data. Remote Sensing of Environment, 2008, 112 (1): 142- 155. doi: 10.1016/j.rse.2007.04.016
	Heinsch F A, Running S W, Kimball J S, 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 (7): 1908- 1925. doi: 10.1109/TGRS.2005.853936
	Hilker T, Gitelson A, Coops N C, et al. Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia, 2011, 165 (4): 865- 876. doi: 10.1007/s00442-010-1901-0
	Hollinger D Y, Ollinger S V, Richardson A D, et al. Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration. Global Change Biology, 2010, 16 (2): 696- 710. doi: 10.1111/j.1365-2486.2009.02028.x
	Horn J E, Schulz K. Identification of a general light use efficiency model for gross primary production. Biogeosciences, 2011, 8 (4): 999- 1021. doi: 10.5194/bg-8-999-2011
	Keenan T F, Williams C A. The terrestrial carbon sink. Annual Review of Environment and Resources, 2018, 43 (1): 219- 243. doi: 10.1146/annurev-environ-102017-030204
	Kergoat L, Lafont S, Arneth A, et al. Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems. Journal of Geophysical Research: Biogeosciences, 2008, 113 (4): G04017.
	Khanal N, Matin M A, Uddin K, et al. A comparison of three temporal smoothing algorithms to improve land cover classification: a case study from Nepal. Remote Sensing, 2020, 12 (18): 2888. doi: 10.3390/rs12182888
	Lasslop G, Reichstein M, Papale D, et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global Change Biology, 2010, 16 (1): 187- 208. doi: 10.1111/j.1365-2486.2009.02041.x
	Liao Z Z, Zhou B H, Zhu J Y, et al. A critical review of methods, principles and progress for estimating the gross primary productivity of terrestrial ecosystems. Frontiers in Environmental Science, 2023, 11, 1093095. doi: 10.3389/fenvs.2023.1093095
	Lü Y L, Chi H, Shi P C, et al. Phenology-based maximum light use efficiency for modeling gross primary production across typical terrestrial ecosystems. Remote Sensing, 2023, 15 (16): 4002. doi: 10.3390/rs15164002
	Madani N, Kimball J S, Affleck D, et al. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency. Journal of Geophysical Research Biogeosciences, 2014, 119 (9): 1755- 1769. doi: 10.1002/2014JG002709
	Madani N, Kimball J S, Running S W. Improving global gross primary productivity estimates by computing optimum light use efficiencies using flux tower data. Journal of Geophysical Research Biogeosciences, 2017, 122 (11): 2939- 2951. doi: 10.1002/2017JG004142
	Migliavacca M, Reichstein M, Richardson A D, et al. Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites. Global Change Biology, 2011, 17 (1): 390- 409. doi: 10.1111/j.1365-2486.2010.02243.x
	Mishra A K. Retrieval of EVI from oceansat 2 data and comparison with MODIS derived EVI. Journal of The Indian Society of Remote Sensing, 2014, 42 (4): 877- 883. doi: 10.1007/s12524-014-0369-5
	Misson L, Baldocchi D D, Black T A, et al. Partitioning forest carbon fluxes with overstory and understory eddy-covariance measurements: a synthesis based on FLUXNET data. Agricultural and Forest Meteorology, 2007, 144 (12): 14- 31.
	Monteith J L. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 1972, 9 (3): 747- 766. doi: 10.2307/2401901
	Nakaji T, Ide R, Oguma H, et al. Utility of spectral vegetation index for estimation of gross CO₂ flux under varied sky conditions. Remote Sensing of Environment, 2007, 109 (3): 274- 284. doi: 10.1016/j.rse.2007.01.006
	Pei Y Y, Dong J W, Zhang Y, et al. Evolution of light use efficiency models: improvement, uncertainties, and imply-cations. Agricultural and Forest Meteorology, 2022, 317, 108905. doi: 10.1016/j.agrformet.2022.108905
	Piao S L, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 2008, 451 (7174): 49- 52. doi: 10.1038/nature06444
	Piao S L, Sitch S, Ciais P, et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO₂ trends. Global Change Biology, 2013, 19 (7): 2117- 2132. doi: 10.1111/gcb.12187
	Prince S D, Goward S N. Global primary production: a remote sensing approach. Journal of Biogeography, 1995, 22, 815. doi: 10.2307/2845983
	Running S W, Nemani R, Heinsch F, et al. A continuous satellite-derived measure of global terrestrial primary production. BioScience, 2004, 54 (6): 547- 560. doi: 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
	Saito M, Maksyutov S, Hirata R, et al. An empirical model simulating diurnal and seasonal CO₂ flux for diverse vegetation types and climate conditions. Biogeosciences, 2009, 6 (4): 585- 599. doi: 10.5194/bg-6-585-2009
	Srinet R, Nandy S, Watham T, et al. Coupling earth observation and eddy covariance data in light use efficiency based model for estimation of forest productivity. Geocarto International, 2022, 37 (25): 7716- 7732. doi: 10.1080/10106049.2021.1983032
	Sun G, Alstad K, Chen J, et al. A general predictive model for estimating monthly ecosystem evapotranspiration. Ecohydrology, 2011, 4 (2): 245- 255. doi: 10.1002/eco.194
	Sun Z Y, Wang X F, Zhang X R, et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO₂ trends. Science of The Total Environment, 2019, 668 (8): 696- 713.
	Wang H B, Xin L, Ma M G, et al. Improving estimation of gross primary production in dryland ecosystems by a model data fusion approach. Remote Sensing, 2019, 11 (3): 225. doi: 10.3390/rs11030225
	Wang H S, Jia G S, Fu C B, et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sensing of Environment, 2010a, 114 (10): 2248- 2258. doi: 10.1016/j.rse.2010.05.001
	Wang K, Dickinson R E, Wild M, et al. Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. model development. Journal of Geophysical Research: Atmospheres, 2010b, 115 (20): D20112.
	Wang L C, Zhu H J, Lin A W, et al. Evaluation of the latest MODIS GPP products across multiple biomes using global eddy covariance flux data. Remote Sensing, 2017, 9 (5): 418. doi: 10.3390/rs9050418
	Wu C Y, Munger J W, Niu Z, et al. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sensing of Environment, 2010, 114 (12): 2925- 2939. doi: 10.1016/j.rse.2010.07.012
	Wu J B, Xiao X M, Guan D X, et al. Estimation of the gross primary production of an old-growth temperate mixed forest using eddy covariance and remote sensing. International Journal of Remote Sensing, 2009, 30 (5): 463- 479.
	Wu Z F. Assessment of eco-climatic suitability and climate change impacts of/on broad-leaved Korean pine forest in northeast China. The Journal of Applied Ecology, 2003, 14 (5): 771- 775.
	Xiao X M, Hollinger D, Aber J, et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 2004a, 89 (4): 519- 534. doi: 10.1016/j.rse.2003.11.008
	Xiao X M, Zhang Q Y, Braswell B, et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 2004b, 91 (2): 256- 270. doi: 10.1016/j.rse.2004.03.010
	Xiao X M, Zhang Q Y, Saleska S, et al. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sensing of Environment, 2005, 94 (1): 105- 122. doi: 10.1016/j.rse.2004.08.015
	Yang D, Xu X L, Xiao F J, et al. Improving modeling of ecosystem gross primary productivity through reoptimizeng temperature restrictions on photosynthesis. Science of The Total Environments, 2021, 788 (3): 147805.
	Yi C X, Ricciuto D, Li R, et al. Climate control of terrestrial carbon exchange across biomes and continents. Environmental Research Letters, 2010, 5 (3): 034007. doi: 10.1088/1748-9326/5/3/034007
	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 (11): 2555- 2571. doi: 10.1111/j.1365-2486.2008.01663.x
	Yu Q Z, Wang S Q, Mickler R A, et al. Narrowband bio-indicator monitoring of temperate forest carbon fluxes in northeastern China. Remote Sensing, 2014, 6 (9): 8986- 9013. doi: 10.3390/rs6098986
	Yuan W P, Liu D, Dong W J, et al. Multiyear precipitation reduction strongly decreases carbon uptake over northern China. Journal of Geophysical Research Biogeosciences, 2014, 119 (5): 881- 896. doi: 10.1002/2014JG002608
	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 (3): 189- 207.
	Yuan W P, Zheng Y, Piao S L, et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sience Advances, 2019, 5 (8): eaax1396.
	Zhang J Y, Wu L Y, Huang G, et al. Relationships between large-scale circulation patterns and carbon dioxide exchange by a deciduous forest. Journal of Geophysical Research Atmospheres, 2011, 116 (4): D04102.
	Zhang Y, Xiao X M, Wu X C, et al. Data descriptor: a global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Scientific Data, 2017, 4, 170165. doi: 10.1038/sdata.2017.165
	Zhou X W, Xin Q C. Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency. International Journal of Remote Sensing, 2019, 40 (3): 931- 955. doi: 10.1080/01431161.2018.1519285

项目Item	长白山 Changbaishan	千烟洲 Qianyanzhou	鼎湖山 Dinghushan	西双版纳 Xishuangbanna
经纬度 Longitude and latitude	128°28′E, 42°24′N	115°03′E, 26°44′N	112°30′E, 23°09′N	101°15′E, 21°55′N
海拔Elevation/m	784	102	280	730
植被类型 Vegetation type	落叶针阔混交林 Deciduous mixed coniferous-broad forests	人工常绿针叶林 Planted evergreen coniferous forest	常绿阔叶林 Evergreen broad-leaved forests	季节性雨林 Seasonal rainforests
优势种 Dominant species	红松 Pinus koraiensis	马尾松 Pinus massoniana	木荷、厚壳桂 Schima superba、Cryptocarya chinensis	番龙眼、千果榄仁 Pometia pinnata、Terminalia myriocarpa
最大叶面积指数Maximum leaf area index	6.1	3.6	4.0	6.0
林冠高度 Forest canopy height/m	26	12	17	36
林龄 Forest age/a	200	35	400	200
年均气温 Mean annual temperature/℃	3.8	18.3	22.3	22.5
年降水量 Mean annual precipitation/mm	744.6	1 380.1	1 888.9	1 427.5
空气相对湿度 Air relative humidity(%)	68.5	83.4	76.8	82.9
来源 Source	常晓晴等 , 2019	刘允芬等 , 2004	魏识广等 , 2022	房秋兰等, 2006

站点 Sites	参数Parameter				参数Parameter
站点 Sites	光合最低温度 Minimum photosynthetic temperature/℃	光合最高温度 Optimum photosynthetic temperature/℃	光合最适温度 Maximum photosynthetic temperature/℃	来源 Source	生长季最大地表水分指数 The maximum land surface water index of the growing season	来源 Source
长白山 Changbaishan	0	35	20	Wu, 2003	0.37	本研究 This study
千烟洲 Qianyanzhou	0	40	20	Xiao et al., 2004a	0.34	本研究 This study
鼎湖山 Dinghushan	2	48	28	Xiao et al., 2005	0.32	本研究 This study
西双版纳 Xishuangbanna	2	48	28	Xiao et al., 2005	0.31	本研究 This study

站点Sites	年份Year	最大光能利用率 Maximum light use efficiency/ （g·mol^?1）	最大生态系统碳总交换速率 Maximum gross ecosystem carbon exchange rate/ (g·m^?2s^?1)	生态系统呼吸速率 Ecosystem respiration rate/ (g·m^?2s^?1)	R²
长白山 Changbaishan	2007	0.92±0.02	0.33	9.27E?02	0.79
	2008	0.71±0.02	0.35	1.09E?01	0.80
	2009	0.91±0.02	0.33	8.73E?02	0.78
	2010	0.78±0.03	0.30	8.45E?02	0.70
千烟洲 Qianyanzhou	2007	0.34±0.01	0.31	7.64E?02	0.78
	2008	0.32±0.02	0.32	8.73E?02	0.78
	2009	0.33±0.01	0.31	7.64E?02	0.77
	2010	0.36±0.01	0.34	8.18E?02	0.80
鼎湖山 Dinghushan	2007	0.35±0.04	0.36	6.00E?02	0.81
	2008	0.33±0.06	0.27	3.55E?02	0.61
	2009	0.33±0.01	0.18	4.09E?02	0.52
	2010	0.33±0.02	0.12	5.18E?02	0.66
西双版纳 Xishuangbanna	2007	0.66±0.03	0.25	8.18E?02	0.28
	2008	0.77±0.04	0.24	7.91E?02	0.35
	2009	0.51±0.03	0.25	8.45E?02	0.38
	2010	0.85±0.04	0.25	7.64E?02	0.31

[1]	Chongfan Guan,Xiang Gao,Zhipeng Li,Xiaochuang Hu,Meijun Hu,Jinsong Zhang,Ping Meng,Jinfeng Cai,Shoujia Sun. Response and Prediction of Productivity and Water Use Efficiency of Pinus sylvestris var. mongolica Plantations in Western Liaoning Province to Climate Change [J]. Scientia Silvae Sinicae, 2024, 60(7): 28-39.
[2]	Jiaming Wan,Jiang Lü,Yun Shi,Hang Xu,Zhiqiang Zhang. Effects of Diffuse Radiation on the Gross Primary Productivity of a Poplar Plantation [J]. Scientia Silvae Sinicae, 2023, 59(5): 1-10.
[3]	Rundong Li,Wendong Tian,Haiqun Yu,Xinhao Li,Chuan Jin,Peng Liu,Tianshan Zha,Yun Tian. Forest Phenology Estimation and Its Relationships with Corresponding Meteorological Factors Based on Digital Images in Songshan, Beijing, China [J]. Scientia Silvae Sinicae, 2022, 58(1): 89-97.
[4]	Yu Bai,Yong Pang,Xiaoyun Xia,Weiwei Jia. 3-PG Model Parameterization Using Destructive Sampling Data of Larix olgensis [J]. Scientia Silvae Sinicae, 2022, 58(1): 98-110.
[5]	Hailian Xue,Xianglin Tian,Tianjian Cao. Optimizing Parameters of a Process-Based Model for Pinus armandii: A Compromise between Empirical and Process-Based Modelling Approaches [J]. Scientia Silvae Sinicae, 2021, 57(9): 21-33.

Impacts of Optimizing Maximum Light Use Efficiency Parameter in VPM on GPP Simulation in Different Forest Ecosystems

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 0

Related Articles 5

Recommended Articles

Metrics

Comments