1Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
4College of Earth Sciences, Chengdu University of Technology, Chengdu 610000, China
6Dinghushan Forest Ecosystem Research Station, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
7Pearl River Delta Forest Ecosystem Research Station, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510650, China
8School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong
9Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
1Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
4College of Earth Sciences, Chengdu University of Technology, Chengdu 610000, China
6Dinghushan Forest Ecosystem Research Station, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
7Pearl River Delta Forest Ecosystem Research Station, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510650, China
8School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong
9Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Received: 11 Dec 2022 – Discussion started: 17 Jan 2023
Abstract. Quantification of large-scale leaf age-dependent leaf area index has been lacking in tropical and subtropical evergreen broadleaved forests (TEFs) despite the recognized importance of leaf age in influencing leaf photosynthetic capacity in this region. Here, we simplified the canopy leaves of TEFs into three age cohorts, i.e., young, mature and old one, with different photosynthesis capacity (Vc,max) and produced a first grid dataset of leaf age-dependent LAI product (referred to as Lad-LAI) over the continental scale from satellite observations of TROPOMI (the TROPOspheric Monitoring Instrument) sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis. The seasonality of three LAI cohorts from the new Lad-LAI products agree well at the three sites (one in subtropical Asia and two in Amazon) with very fine collections of monthly LAI of young, mature and old leaves. Continental-scale comparisons with independent Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) products and 53 samples of in situ measurements of seasonal litterfall data also demonstrate the robustness of the LAI seasonality of the three leaf age cohorts. The spatial patterns clustered from the three LAI cohorts coincides with those clustered from climatic variables. And the young and mature LAI cohorts perform well in capturing a dry-season green-up of canopy leaves across the wet Amazonia areas where mean annual precipitation exceeds 2,000 mm yr−1, consistent with previous satellite data analysis. The new Lad-LAI products are primed to diagnose the adaption of tropical and subtropical forest to climate change; and will also help improve the development of phenology modules in Earth System Models. The proposed satellite-based approaches can provide reference for mapping finer temporal and spatial resolution LAI products with different leaf age cohorts. The Lad-LAI products are available at https://doi.org/10.6084/m9.figshare.21700955.v2 (Yang et al., 2022).
We for the first-time mapped continental-scale grid dataset of monthly LAI in three leaf age cohorts, i.e., young, mature and old one, from 2001–2018 RTSIF data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI products agree well at the three sites with very fine collections of monthly LAI. The proposed satellite-based approaches can provide reference for mapping finer temporal and spatial resolution LAI products with different leaf age cohorts.
We for the first-time mapped continental-scale grid dataset of monthly LAI in three leaf age...