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Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data

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https://ir.library.oregonstate.edu/concern/articles/x920fz64b

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  • Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the global carbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal variation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporal scales. In this context, understanding the strengths and weaknesses of remote sensing-based models of GPP and improving their performance is a key contemporary scientific activity. We used measurements from 157 research sites (∼470 site-years) in the FLUXNET “La Thuile” data and compared the skills of 11 different remote sensing models in capturing intra- and inter-annual variations in daily GPP in seven different biomes. Results show that the models were able to capture significant intra-annual variation in GPP (Index of Agreement = 0.4–0.80) in all biomes. However, the models’ ability to track inter-annual variation in daily GPP was significantly weaker (IoA < 0.45). We examined whether the inclusion of different mechanisms that are missing in the models could improve their predictive power. The mechanisms included the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differential rates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmental variables, better representation of soil-moisture dynamics, and allowing spatial variation in model parameters. Our analyses suggest that the next generation remote sensing models need better representation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studies may not have significant bearing on model performance at continental and global scales. Understanding the relative controls of biotic vis-a-vis abiotic factors on GPP and accurately scaling up leaf level processes to the ecosystem scale are likely to be important for recognizing the limitations of remote sensing model and improving their formulation.
  • KEYWORDS: FLUXNET, Seasonal, Lagged effects, Remote sensing, Modeling, Gross primary productivity
  • This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/agricultural-and-forest-meteorology/
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  • Verma, M., Friedl, M. A., Law, B. E., Bonal, D., Kiely, G., Black, T. A., ... & D'Odorico, P. (2015). Improving the performance of remote sensing models for capturing intra-and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data. Agricultural and Forest Meteorology, 214, 416-429. doi:10.1016/j.agrformet.2015.09.005
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  • This research was partially supported by NASA grant number NNX11AE75G, the National Science Foundation Macrosystem Biology program (award EF-1065029), and AmeriFlux [the Office of Science (BER), US Department of Energy (DOE; DE-FG02-04ER63917 and DE-FG02-04ER63911)]. MV and MAF gratefully acknowledge the efforts of the FLUXNET community to compile and make available the La Thuile data set. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, Asia Flux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, China Flux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), Green Grass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Universite Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California Berkeley and the University of Virginia.
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