Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China
文献类型：期刊论文
| 作者 | Xie, Xinyao1,2 ; Li, Ainong2; Jin, Huaan2; Bian, Jinhu2; Zhang, Zhengjian1,2; Nan, Xi2
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 13期号:18页码:25 |
| ISSN号 | 2072-4292 |
| 关键词 | GPP estimation mountainous areas ecosystem models remote sensing |
| DOI | 10.3390/rs13183567 |
| 英文摘要 | Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02-0.29 and root mean square errors ranging from 399-821 gC m(-2)yr(-1). These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models. |
| WOS关键词 | USE EFFICIENCY MODEL ; NET PRIMARY PRODUCTIVITY ; LAND-SURFACE TEMPERATURE ; ENHANCED VEGETATION INDEX ; COVARIANCE FLUX DATA ; TERRESTRIAL ECOSYSTEMS ; SOLAR-RADIATION ; TOPOGRAPHIC CORRECTION ; BOREAL ECOSYSTEM ; AIR-TEMPERATURE |
| 资助项目 | National Key Research and Development Program of China[2020YFA0608700] ; National Key Research and Development Program of China[2016YFA0600103] ; National Natural Science Foundation of China[41631180] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2019365] ; Chinese Academy of Sciences Light of West China Program |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000700059000001 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Chinese Academy of Sciences Light of West China Program |
| 源URL | [http://ir.imde.ac.cn/handle/131551/56128]  |
| 专题 | 成都山地灾害与环境研究所_数字山地与遥感应用中心 |
| 通讯作者 | Li, Ainong |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Xinyao,Li, Ainong,Jin, Huaan,et al. Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China[J]. REMOTE SENSING,2021,13(18):25. |
| APA | Xie, Xinyao,Li, Ainong,Jin, Huaan,Bian, Jinhu,Zhang, Zhengjian,&Nan, Xi.(2021).Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China.REMOTE SENSING,13(18),25. |
| MLA | Xie, Xinyao,et al."Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China".REMOTE SENSING 13.18(2021):25. |
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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