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A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.
Tian, Jie; Yang, Xueqin; Yuan, Wenping; Lin, Shangrong; Han, Liusheng; Zheng, Yi; Xia, Xiaosheng; Liu, Liyang; Wang, Mei; Zheng, Wei; Fan, Lei; Yan, Kai; Chen, Xiuzhi.
Afiliación
  • Tian J; 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), Zhuha
  • Yang X; 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), Zhuha
  • Yuan W; Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China.
  • Lin S; University of Chinese Academy of Sciences, Beijing, China.
  • Han L; College of Urban and Environmental Sciences, Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, Peking University, Beijing, China.
  • Zheng Y; Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China.
  • Xia X; School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo, China.
  • Liu L; 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), Zhuha
  • Wang M; 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), Zhuha
  • Zheng W; Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, France.
  • Fan L; 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), Zhuha
  • Yan K; 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), Zhuha
  • Chen X; Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China.
Glob Chang Biol ; 30(8): e17454, 2024 Aug.
Article en En | MEDLINE | ID: mdl-39132898
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 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estaciones del Año / Bosques / Hojas de la Planta / Tecnología de Sensores Remotos Idioma: En Revista: Glob Chang Biol Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estaciones del Año / Bosques / Hojas de la Planta / Tecnología de Sensores Remotos Idioma: En Revista: Glob Chang Biol Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido