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

季节性 生产力 常绿 初级生产 泛热带 初级生产力 常绿森林 环境科学 大气科学 气候学 地理 遥感 生态学 生物 经济 生态系统 地质学 宏观经济学
作者
Jie Tian,Xueqin Yang,Wenping Yuan,Shangrong Lin,Liusheng Han,Yi Zheng,Xiaosheng Xia,Liyang Liu,Mei Wang,Wei Zheng,Lei Fan,Kai Yan,Xiuzhi Chen
出处
期刊:Global Change Biology [Wiley]
卷期号:30 (8) 被引量:1
标识
DOI:10.1111/gcb.17454
摘要

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 2 = .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 R 2 = .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 ( R 2 = .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) ( R 2 = .64; average RMSE = 1.90 gC m −2 day −1 ) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) ( R 2 = .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.
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