English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

MPS-Authors
/persons/resource/persons62425

Jung,  Martin
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A., et al. (2014). Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 111(14), E1327-E1333. doi:10.1073/pnas.1320008111.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0018-4771-C
Abstract
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-ofthe-
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.