A dynamic-leaf light use efficiency model for improving gross primary production estimation
文献类型：期刊论文
| 作者 | Huang, Lingxiao1; Yuan, Wenping2; Zheng, Yi3; Zhou, Yanlian4,5; He, Mingzhu6; Jin, Jiaxin7; Huang, Xiaojuan8; Chen, Siyuan9,10; Liu, Meng; Guan, Xiaobin12 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2024 |
| 卷号 | 19期号:1页码:14066 |
| 关键词 | gross primary production light use efficiency (LUE) models dynamic-leaf LUE model big-leaf and two-leaf LUE models sunlit and shaded leaves |
| DOI | 10.1088/1748-9326/ad1726 |
| 产权排序 | 1 |
| 英文摘要 | Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a 'potential' sunlit leaf area index (LAIsu) can separate a portion of LAIsu even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an 'effective' LAIsu to improve GPP estimation, which considers the comprehensive contribution of LAIsu when the canopy does and does not receive direct radiation. In particular, the new model decreases LAIsu to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m-2 d-1 and 1.53 g C m-2 d-1 to 1.36 g C m-2 d-1 and increasing the coefficient of determination (R 2) from 0.74 and 0.79-0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m-2 month-1 and28.11 g C m-2 month-1 to 25.81 g C m-2 month-1 at a monthly scale and from 231.82 g C m-2 yr-1 and 221.60 g C m-2 yr-1-200.00 g C m-2 yr-1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m-2 d-1 and 0.46 g C m-2 d-1, respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07-0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components. |
| WOS关键词 | RATE V-CMAX ; SHADED LEAVES ; PHOTOSYNTHESIS ; INDEX ; SUNLIT ; CARBON ; FLUX ; FLUORESCENCE |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001138594200001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201683]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100101, Peoples R China 3.Peking Univ, Inst Carbon Neutral, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100091, Peoples R China 4.Sun Yat Sen Univ, Sch Atmospher Sci, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 5.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Key Lab Land Satellite Remote Sensing Applicat, Sch Geog & Ocean Sci,Minist Nat Resources, Nanjing 210023, Peoples R China 6.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 7.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Zhuhai 519087, Peoples R China 8.Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Peoples R China 9.Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China 10.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Lingxiao,Yuan, Wenping,Zheng, Yi,et al. A dynamic-leaf light use efficiency model for improving gross primary production estimation[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(1):14066. |
| APA | Huang, Lingxiao.,Yuan, Wenping.,Zheng, Yi.,Zhou, Yanlian.,He, Mingzhu.,...&Tang, Ronglin.(2024).A dynamic-leaf light use efficiency model for improving gross primary production estimation.ENVIRONMENTAL RESEARCH LETTERS,19(1),14066. |
| MLA | Huang, Lingxiao,et al."A dynamic-leaf light use efficiency model for improving gross primary production estimation".ENVIRONMENTAL RESEARCH LETTERS 19.1(2024):14066. |
入库方式： OAI收割
来源：地理科学与资源研究所
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