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Remote Sens. 2018, 10(9), 1346; https://doi.org/10.3390/rs10091346

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

1
Goddard Space Flight Center, Greenbelt, MD 20771, USA
2
Science Systems Applications, Inc. (SSAI), Lanham, MD 20706, USA
3
Columbia University, Earth and Environmental Engineering, New York, NY 10027, USA
4
European Commission Joint Research Centre, Institute for Environment and Sustainability, I-21027 Ispra, Italy
5
Max Planck Institute, 07745 Jena, Germany
6
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Received: 14 June 2018 / Revised: 13 August 2018 / Accepted: 19 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Abstract

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates. View Full-Text
Keywords: gross primary production; GPP; NDVI; vegetation indices; solar-induced fluorescence; MODIS; light-use efficiency; satellite reflectance; NIRV gross primary production; GPP; NDVI; vegetation indices; solar-induced fluorescence; MODIS; light-use efficiency; satellite reflectance; NIRV
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Joiner, J.; Yoshida, Y.; Zhang, Y.; Duveiller, G.; Jung, M.; Lyapustin, A.; Wang, Y.; Tucker, C.J. Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data. Remote Sens. 2018, 10, 1346.

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