Knowledge Management System of Northwest Institute of Plateau Biology, CAS
| A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau | |
| Gao, Yanni1,2; Yu, Guirui1; Li, Shenggong1; Yan, Huimin1; Zhu, Xianjin1,3; Wang, Qiufeng1; Shi, Peili1; Zhao, Liang4; Li, Yingnian4; Zhang, Fawei4; Wang, Yanfen3; Zhang, Junhui5 | |
| 2015-05-24 | |
| 发表期刊 | ECOLOGICAL MODELLING
 |
| 卷号 | 304页码:34-43 |
| 文章类型 | Article |
| 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. |
| 关键词 | Ecosystem Respiration Gross Primary Production Temperature Modis Data Chinaflux Pcm |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 关键词[WOS] | GROSS PRIMARY PRODUCTION ; SPECTRAL VEGETATION INDEXES ; FOREST CARBON BALANCE ; SOIL ORGANIC-CARBON ; TEMPERATURE SENSITIVITY ; CO2 EXCHANGE ; HETEROTROPHIC COMPONENTS ; PHOTOSYNTHESIS CONTROLS ; SURFACE-TEMPERATURE ; CROPPING SYSTEMS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS类目 | Ecology |
| WOS记录号 | WOS:000353747800004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://210.75.249.4/handle/363003/5553 |
| 专题 | 中国科学院西北高原生物研究所 |
| 作者单位 | 1.Chinese Acad Sci, Synth Res Ctr Chinese Ecosyst Res Network, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining 810001, Peoples R China 5.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China |
| 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,2015,304:34-43. |
| 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,304,34-43. |
| MLA | Gao, Yanni,et al."A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau".ECOLOGICAL MODELLING 304(2015):34-43. |
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