A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems
文献类型：期刊论文
| 作者 | Huang, Lingxiao1,2; Lin, Xiaofeng3,4; Jiang, Shouzheng5,6; Liu, Meng7; Jiang, Yazhen1,2; Li, Zhao-Liang1,7; Tang, Ronglin1,2 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 17期号:10页码:11 |
| ISSN号 | 1748-9326 |
| 关键词 | gross primary production maximum light-use efficiency two-stage light-use efficiency model seasonal fluctuations agroecosystems |
| DOI | 10.1088/1748-9326/ac8b98 |
| 通讯作者 | Tang, Ronglin(tangrl@lreis.ac.cn) |
| 英文摘要 | Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE (epsilon(max)), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of epsilon(max), and (b) separately re-parameterizing epsilon(max) in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art epsilon(max)-static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three epsilon(max)-static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three epsilon(max)-static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m(-2) d(-1)) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIRv) were used to re-parameterize the epsilon(max), respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of epsilon(max) and indicates great potential for improving daily agroecosystem GPP estimates at a global scale. |
| WOS关键词 | AREA INDEX PRODUCT ; RATE V-CMAX ; RESPONSE CURVE ; FLUX ; ECOSYSTEM ; CONVERGENCE ; SCALE |
| 资助项目 | National Natural Science Foundation of China[41922009] ; National Natural Science Foundation of China[42071332] ; National Key R&D Program of China[2018YFA0605401] |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | IOP Publishing Ltd |
| WOS记录号 | WOS:000860831400001 |
| 资助机构 | National Natural Science Foundation of China ; National Key R&D Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/184972]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Ronglin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100101, Peoples R China 3.Jimei Univ, Coll Harbor & Coastal Engn, Polar & Marine Res Inst, Xiamen 361021, Peoples R China 4.Jimei Univ, Natl Geog Condit Monitoring Res Ctr, Xiamen 361021, Peoples R China 5.Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China 6.Sichuan Univ, Coll Water Resource & Hydropower, Chengdu 610065, Peoples R China 7.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Lingxiao,Lin, Xiaofeng,Jiang, Shouzheng,et al. A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems[J]. ENVIRONMENTAL RESEARCH LETTERS,2022,17(10):11. |
| APA | Huang, Lingxiao.,Lin, Xiaofeng.,Jiang, Shouzheng.,Liu, Meng.,Jiang, Yazhen.,...&Tang, Ronglin.(2022).A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems.ENVIRONMENTAL RESEARCH LETTERS,17(10),11. |
| MLA | Huang, Lingxiao,et al."A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems".ENVIRONMENTAL RESEARCH LETTERS 17.10(2022):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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