Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence

Zarco-Tejada, Pablo ; Jung, Martin ; Voigt, Maximilian ; Huete, Alfredo R. ; Berry, Joseph A. ; Beer, Christian ; Klumpp, Katja ; Griffis, Timothy J. ; Guanter, Luis ; Camps-Valls, Gustavo ; Ponce-Campos, Guillermo ; Baker, John M. ; Joiner, Joanna ; Lee, Jung-Eun ; Cescatti, Alessandro ; Moran, M. Susan ; Gianelle, Damiano ; Frankenberg, Christian ; Zhang, Yongguang ; Buchmann, Nina

PID  :  NTRS Document ID: 20140012654
RP-ID  :  GSFC-E-DAA-TN13588

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.