NWIPB OpenIR
A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau
Gao, Yanni ; Yu, Guirui ; Li, Shenggong ; Yan, Huimin ; Zhu, Xianjin ; Wang, Qiufeng ; Shi, Peili ; Zhao, Liang ; Li, Yingnian ; Zhang, Fawei ; Wang, Yanfen ; Zhang, Junhui
2015-05-24
发表期刊ECOLOGICAL MODELLING
摘要Ecosystem respiration (R-e) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R-e (ReRSM) by assuming that one part of R-e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R-e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R-e agreed well with the observed R-e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R-e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol Cm-2 d(-1) and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R-e across five vegetation types, with the RMSE of 0.05 mol Cm-2 d(-1) and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in Re estimation and has the potential of estimating spatial R-e. However, the performance of ReRSM in other vegetation types or regions still needs a further study. (C) 2015 Elsevier B.V. All rights reserved.; Ecosystem respiration (R-e) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R-e (ReRSM) by assuming that one part of R-e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R-e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R-e agreed well with the observed R-e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R-e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol Cm-2 d(-1) and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R-e across five vegetation types, with the RMSE of 0.05 mol Cm-2 d(-1) and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in Re estimation and has the potential of estimating spatial R-e. However, the performance of ReRSM in other vegetation types or regions still needs a further study. (C) 2015 Elsevier B.V. All rights reserved.
文献类型期刊论文
条目标识符http://210.75.249.4/handle/363003/27527
专题中国科学院西北高原生物研究所
推荐引用方式
GB/T 7714
Gao, Yanni,Yu, Guirui,Li, Shenggong,et al. A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau[J]. ECOLOGICAL MODELLING,2015.
APA Gao, Yanni.,Yu, Guirui.,Li, Shenggong.,Yan, Huimin.,Zhu, Xianjin.,...&Zhang, Junhui.(2015).A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau.ECOLOGICAL MODELLING.
MLA Gao, Yanni,et al."A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau".ECOLOGICAL MODELLING (2015).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Gao, Yanni]的文章
[Yu, Guirui]的文章
[Li, Shenggong]的文章
百度学术
百度学术中相似的文章
[Gao, Yanni]的文章
[Yu, Guirui]的文章
[Li, Shenggong]的文章
必应学术
必应学术中相似的文章
[Gao, Yanni]的文章
[Yu, Guirui]的文章
[Li, Shenggong]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。