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

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 R<sup>2</sup> = .59, average root-mean-square error [RMSE] = 1.23 gC m<sup>-2</sup> day<sup>-1</sup>) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R<sup>2</sup> = .18; average RMSE = 1.87 gC m<sup>-2</sup> day<sup>-1</sup>). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R<sup>2</sup> = .80; average RMSE = 1.74 gC m<sup>-2</sup> day<sup>-1</sup>), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R<sup>2</sup> = .64; average RMSE = 1.90 gC m<sup>-2</sup> day<sup>-1</sup>) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R<sup>2</sup> = .78; average RMSE = 1.88 gC m<sup>-2</sup> day<sup>-1</sup>). 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.

Authors

Institutions

Publication Details

Journal
Global Change Biology
Published
2024-08-01
DOI
https://doi.org/10.1111/gcb.17454
Citations
7
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Liusheng Han, Mei Wang, Jie Tian, Yi Zheng et al.
7 citations
Global Change Biology
Remote Sensing in Agriculture
3.21
article

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

Liusheng Han, Mei Wang, Jie Tian, Yi Zheng, Shangrong Lin, Xiaosheng Xia, Kai Yan, Wenping Yuan, Lei Fan, Wei Zheng, Xiuzhi Chen, Liyang Liu, Xueqin Yang
article en
7 citations

Abstract

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 R<sup>2</sup> = .59, average root-mean-square error [RMSE] = 1.23 gC m<sup>-2</sup> day<sup>-1</sup>) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R<sup>2</sup> = .18; average RMSE = 1.87 gC m<sup>-2</sup> day<sup>-1</sup>). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R<sup>2</sup> = .80; average RMSE = 1.74 gC m<sup>-2</sup> day<sup>-1</sup>), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R<sup>2</sup> = .64; average RMSE = 1.90 gC m<sup>-2</sup> day<sup>-1</sup>) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R<sup>2</sup> = .78; average RMSE = 1.88 gC m<sup>-2</sup> day<sup>-1</sup>). 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.

Global Change BiologyVol. 30(8)
Shandong University of Technology (CN), Centre National de la Recherche Scientifique (FR), Southwest University (CN), Sun Yat-sen University (CN), Université de Versailles Saint-Quentin-en-Yvelines (FR), Chinese Academy of Sciences (CN), Peking University (CN), Beijing Normal University (CN), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Guangzhou Institute of Geochemistry (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), University of Chinese Academy of Sciences (CN), State Key Laboratory of Remote Sensing Science (CN), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Remote Sensing in Agriculture
3.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.