Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 498037
Title Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP : An analysis using global FLUXNET tower data
Author(s) Verma, M.; Friedl, M.A.; Law, B.E.; Bonal, D.; Kiely, G.; Black, T.A.; Wohlfahrt, G.; Moors, E.J.; Montagnani, L.; Marcolla, B.; Toscano, P.; Varlagin, A.; Roupsard, O.; Cescatti, A.; Arain, M.A.; Odorico, P. D'
Source Agricultural and Forest Meteorology 214-215 (2015). - ISSN 0168-1923 - p. 416 - 429.
DOI https://doi.org/10.1016/j.agrformet.2015.09.005
Department(s) Alterra - Climate change and adaptive land and water management
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) FLUXNET - Gross primary productivity - Lagged effects - Modeling - Remote sensing - Seasonal
Abstract

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.

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