Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas
文献类型：期刊论文
| 作者 | Xie Xinyao1,2; Li Ainong1 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2020 |
| 卷号 | 295页码:108193 |
| 关键词 | Gross primary productivity (GPP) Temperature and greenness model Remote sensing Mountainous areas Topography |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2020.108193 |
| 产权排序 | 1 |
| 通讯作者 | Li, Ainong(ainongli@imde.ac.cn) |
| 文献子类 | Article |
| 英文摘要 | The temperature and greenness model (TG) demonstrates that the combination of enhanced vegetation index and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is feasible in obtaining gross primary productivity (GPP) at the landscape, regional, and global scales. However, the input LST data of TG is always available at a coarse resolution (similar to 1 km), averaging a relatively large portion of the topographic characteristics. Hence, GPP simulated using the coarse spatial resolution LST data would suffer from unavoidable bias over mountainous areas. Considering the above limitation, this work proposed a mountainous temperature and greenness model (MTG) through integrating an elevation-corrected factor and a radiation-corrected factor with the current TG model. The proposed MTG model was validated at sixteen eddy covariance (EC) sites with apparent topography in the carbon footprint areas. Results showed that MTG-simulated GPP presented a better agreement with EC GPP than TG-simulated GPP, characterized by an increase of 0.06 in R-2 and a decrease of 5.43 gC m(-2) 8d(-1) in root mean square error, suggesting that the MTG model had a better feasibility of capturing the GPP variations over mountainous areas than the TG model. The standard deviation of MTG-simulated GPP at the sixteen study sites varied between 3.29 and 22.79 gC m(-2) 8d(-1), highlighting the importance of considering topography within coarse pixels when obtaining GPP estimates over mountainous areas. Furthermore, results also indicated that the MTG-simulated GPP showed obvious responses to topography, suggesting that the MTG model could adequately characterize the topographic effects on plant photosynthesis. More specifically, MTG-simulated GPP increased when slope increased in the sunlit terrains, while it was found to have a lower value when slope increased in the shaded terrains. Our study suggests that incorporating topography information into current GPP models is a practical approach to improve GPP estimates over mountainous areas. |
| 电子版国际标准刊号 | 1873-2240 |
| WOS关键词 | GROSS PRIMARY PRODUCTION ; LIGHT-USE EFFICIENCY ; LAND-SURFACE TEMPERATURE ; ENHANCED VEGETATION INDEX ; CARBON-DIOXIDE EXCHANGE ; NET ECOSYSTEM EXCHANGE ; PRIMARY PRODUCTIVITY ; EDDY-COVARIANCE ; SOIL-MOISTURE ; BOREAL ECOSYSTEM |
| 资助项目 | National Natural Science Foundation of China[41631180] ; National Key Research and Development Program of China[2016YFA0600103] ; China Scholarship Council |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000582305500025 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; China Scholarship Council |
| 源URL | [http://ir.imde.ac.cn/handle/131551/46860]  |
| 专题 | 中国科学院水利部成都山地灾害与环境研究所 |
| 通讯作者 | Li Ainong |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie Xinyao,Li Ainong. Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas[J]. AGRICULTURAL AND FOREST METEOROLOGY,2020,295:108193. |
| APA | Xie Xinyao,&Li Ainong.(2020).Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas.AGRICULTURAL AND FOREST METEOROLOGY,295,108193. |
| MLA | Xie Xinyao,et al."Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas".AGRICULTURAL AND FOREST METEOROLOGY 295(2020):108193. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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