| Literature DB >> 33149371 |
Liming He1,2, Jing M Chen1,3, Jane Liu1, Ting Zheng4, Rong Wang1, Joanna Joiner5, Shuren Chou6, Bin Chen7, Yang Liu7, Ronggao Liu7, Cheryl Rogers1.
Abstract
Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, Vcmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring Vcmax is time-consuming and laborious; therefore, the spatiotemporal distribution of Vcmax is still poorly understood due to limited measurements of Vcmax. In this study, we used a data assimilation approach to map the spatial variation of Vcmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived Vcmax map, the mean Vcmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived Vcmax dataset by Kattge et al. (2009). The gradient of Vcmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of Vcmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high Vcmax values, is highlighted in this Vcmax map. Legume plants are characterized with high Vcmax values. This Vcmax map also clearly illustrates the emerging soybean revolution in South America where Vcmax is the highest among the world. The gradient of Vcmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. Vcmax, for global climate change studies.Entities:
Keywords: Chlorophyll fluorescence; Climate change; Ecosystem; Global; Maximum carboxylation rate; Photosynthetic capacity; Vcmax
Year: 2019 PMID: 33149371 PMCID: PMC7608051 DOI: 10.1016/j.rse.2019.111344
Source DB: PubMed Journal: Remote Sens Environ ISSN: 0034-4257 Impact factor: 10.164