[Reddyproc-users] gap filling of several months of missing data

[Thomas Wutzler]

Dear Terenzio,

long gaps are filled by a lookup table and the mean diurnal cycle.
If your measurements are close in environmental space (incoming
radiation (Rg), temperature, ...) then REddyProc can do a decent job
despite distance in time.

For a comparison of gap-filling techniques see the Paper of Moffat et
al. 2012:
https://doi.org/10.1016/j.agrformet.2007.08.011

Probably, the machine learning has gotten better in the mean time.

I resend this to reddyproc-users at lists.r-forge.r-project.org so that you
may get feedback and experience from a wider community.

Yours
Thomas



On 21.11.19 20:28, Zenone, Terenzio wrote:
> Hello
> I'm currently working on a dataset of EC CO2 data collected in a
> mangrove forest: we do have measured data only in january june july and
> august of one year. The methodology implemented in the R package can
> filled such longs gaps to get the annual NEE
> or is better use a Neural network ?
> 
> Thanks for you support
> all the best
> 
> Terenzio
> 
> **************************************************
> 
> Terenzio Zenone PhD
> 
> Research Fellow, College of Life and Environmental Sciences
> 
> University of Exeter (UK)
> 
> Visiting scientist San Diego State University (CA, USA)
> 
> Global Change Research Group 
> email: T.zenone at exeter.ac.uk
> Phone +1 907 855 1209
> 
> *************************************************
> 
> 

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