A leaf age‐dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests,Global Change Biology

当前位置： X-MOL 学术 › Glob. Change Biol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A leaf age‐dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests
Global Change Biology ( IF 10.8 ) Pub Date : 2024-08-12 , DOI: 10.1111/gcb.17454
Jie Tian ₁ , Xueqin Yang _{1,

2,

3} , Wenping Yuan ₄ , Shangrong Lin ₅ , Liusheng Han ₆ , Yi Zheng ₁ , Xiaosheng Xia ₁ , Liyang Liu ₇ , Mei Wang ₁ , Wei Zheng ₁ , Lei Fan ₈ , Kai Yan ₉ , Xiuzhi Chen ₁

Affiliation

Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China.
University of Chinese Academy of Sciences, Beijing, China.
College of Urban and Environmental Sciences, Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, Peking University, Beijing, China.
Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China.
School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo, China.
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, France.
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China.
Faculty of Geographical Science, State Key Laboratory of Remote Sensing Science, Innovation Research Center of Satellite Application (IRCSA), Beijing Normal University, Beijing, China.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one‐third of terrestrial gross primary productivity (GPP). However, the continental‐scale leaf phenology‐photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC‐LUE) and proposed a novel leaf age‐dependent LUE model (denoted as LA‐LUE model). At the site level, the LA‐LUE model (average R2 = .59, average root‐mean‐square error [RMSE] = 1.23 gC m−2 day−1) performs better than the EC‐LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m−2 day−1). At the continental scale, the monthly GPP estimates from the LA‐LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m−2 day−1), and satellite‐based GPP data retrieved from the global Orbiting Carbon Observatory‐2 (OCO‐2) based solar‐induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m−2 day−1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m−2 day−1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite‐based LUE model and provides a feasible implementation for mapping the continental‐scale GPP seasonality over the entire TEFs.

中文翻译：

泛热带常绿阔叶林总初级生产力季节性遥感的叶龄依赖光利用效率模型

热带和亚热带常绿阔叶林（TEF）贡献了陆地总初级生产力（GPP）的三分之一以上。然而，迄今为止，人们对 TEF 上的大陆尺度叶片物候-光合作用关系仍知之甚少。这种知识差距阻碍了大多数光利用效率 (LUE) 模型准确模拟 TEF 中的 GPP 季节性。叶龄是将叶物候动态与 GPP 季节性联系起来的关键植物性状。因此，在这里，我们将不同叶龄群体的季节性叶面积指数纳入广泛使用的LUE模型（即EC-LUE）中，并提出了一种新颖的叶龄依赖性LUE模型（表示为LA-LUE模型）。在站点层面，LA‐LUE 模型（平均右2 = .59，平均均方根误差 [RMSE] = 1.23 gC m −2天−1 ）在模拟九个 TEF 站点的 GPP 季节性（平均右2 = .18；平均 RMSE = 1.87 gC m −2天−1 ）。在大陆尺度上，LA‐LUE 模型的月度 GPP 估计值与 FLUXCOM GPP 数据一致（右2 = .80；平均 RMSE = 1.74 gC m −2天−1 ），以及从全球轨道碳观测站‐2（OCO‐2）基于太阳诱导叶绿素荧光（SIF）产品（GOSIF）检索到的基于卫星的 GPP 数据（右2 = .64；平均 RMSE = 1.90 gC m −2天−1 ）和使用机器学习算法（RTSIF）重建的对流层监测仪器 SIF 数据集（右2 = 。78;平均 RMSE = 1.88 gC m −2天−1 ）。通常，估计的月度 GPP 不仅成功地代表了北回归线和摩羯座附近的单峰 GPP 季节性，而且还很好地捕捉了赤道附近的双峰 GPP 季节性。总体而言，这项研究首次将叶龄信息整合到基于卫星的 LUE 模型中，并为绘制整个 TEF 的大陆尺度 GPP 季节性提供了可行的实现。

更新日期：2024-08-12

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南