A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization

By Dario Papale, R. Valentini. 2003

558 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2a5aee11e2a169c6578f60c377ce1110f3ee7c67

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Citations in the corpus

‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, methodology): “The landscape mosaic captured by remote-sensing pixels, model-grid pixels or flux footprints consist of multiple carbon sinks and sources, operating simultaneously and responding differentially to the environment (Papale and Valentini 2003; Soegaard et al. 2003).”
Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model (2009, background): “Upscaling exercises of eddy covariance based carbon ﬂuxes to large regions has been conducted for the US (Xiao et al., 2008, Yang et al., 2007) and Europe (Jung et al., 2008; Papale and Valentini, 2003; Vetter et al., 2008), which are both characterized by a comparatively dense network of towers.”
Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals (2008, methodology): “The ANN (Papale and Valentini, 2003) is essentially a multi-stage, empirical, non-linear regression model.”
Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes (2007, methodology): “In a companion paper (Desai et al., submitted for publication), the NEE partitioning into GPP and ER of the NLR techniques but also of UKF, ANN_PS, LUT, MDV, SPM, and BETHY has been further investigated.”
Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non‐water‐stressed conditions (2007, background): “Further details of forest site comparisons have been discussed for earlier periods in Papale & Valentini (2003) and Valentini (2003).”
The utility of the eddy covariance techniques as a tool in carbon accounting: tropical savanna as a case study (2005, methodology): “Artificial neural network analyses (Papale and Valentini 2003)were used at both sites to developgap-filling algorithms and corrections to nocturnal CO2 fluxes (Baldocchi et al.”
Improving land surface models with FLUXNET data (2009, background): “Different upscaling approaches exist, ranging from simple regression models, to complex machine learning algorithms (Papale and Valentini, 2003; Yang et al., 2007), The results of such data-oriented models can be used to evaluate process-oriented LSMs operating at regional scales.”
Intensive monitoring of forest ecosystems in Europe: 1. Objectives, set-up and evaluation strategy (2003, methodology): “Interestingly, however, the same kind of pattern is found by Papale and Valentini (2003), presenting spatial (1 x 1km) estimates of carbon fluxes of European forests based on the net CO2 exchange flux collected at sixteen of sites in the EUROFLUX network, using neural networks for the spatial extrap”
Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval (2008, background): “These measurements provide crucial information needed for modeling ecosystem processes and interactions with the atmosphere and upscaling of flux processes for regionalscale carbon balance estimates [Papale and Valentini, 2003; Tenhunen et al., 1998].”