Coarse scaled NDVI (or FAPAR, EVI…) images have a high temporal frequency and are delivered as Maximum Value Composites (MVC) of several days, which means the highest value is taken, assuming that clouds and other contaminated pixels have low values. However, especially in areas with a rainy season, the composites over 10-16 days still contain clouds and other disturbances. This figure illustrates a raw MODIS NDVI MVC of 16 days, and it’s obvious that several pixels are contaminated, and it’s also obvious that analyses will be affected by the noisy data.

Datasets like GIMMS or GEOV1 provide already filtered data, but e.g. MODIS, SPOT VGT, PROBA-V and AVHRR data are raw. The solution is to smooth the time series, using a filter, which calculates a smooth time series and interpolates the bad values using the previous and following images. Here is the same image, but smoothed with a Savitzky Golay filter.

The data are mostly delivered with quality rasters, rating the quality of each pixel. This can be used to either filter the raster and set bad quality pixels to NA, or to weight the pixels. When the new time line is calculated, low weighted (i.e. contaminated) pixels are less considered in the calculation process. One possibility is the software TIMESAT (Eklundh & Jönsson, 2011), which offers different filter techniques. Here is an example how timesat smoothes the time line with a Savitzky Golay filter, omitting “bad” pixles and creating new rasters.

Filtering is also possible in R, and it’s very simple. First one has to decide if one wants to work with quality files, or simply use the raw data, both is possible. GIS software like GRASS has modules which allow an easy use of the quality files:

http://grass.osgeo.org/grass64/manuals/i.in.spotvgt.html

http://grass.osgeo.org/grass70/manuals/i.modis.qc.html

However, filtering without quality flags also provides reasonable results. Now we assume we have a time series of MODIS data from 2005-2010, with 23 images each year. This data is loaded in R in a raster stack or brick, called MODIS, bad values are masked as NA. We load the libraries, and create a function which uses the Savitzky Golay filter from the signal package. The parameters of the function need to be adapted (p, n, ts) (http://www.inside-r.org/packages/cran/signal/docs/sgolayfilt), also the time frame.

*library(raster)*

* library(rgdal)*

* library(signal)*

*library(zoo)*

*fun <- function(x) {*

* v=as.vector(x)*

* z=substituteNA(v, type=”mean”)*

* MODIS.ts2 = ts(z, start=c(2005,1), end=c(2010,23), frequency=23)*

* x=sgolayfilt(MODIS.ts2, p=1, n=3, ts=30)*

* }*

*MODIS.filtered <- calc(MODIS, fun)*

MODIS.filtered is a new brick containing the smoothed time series. Compare the raw with the filtered tims series:

*l=cellStats(MODIS, stat=mean)*

* MODIS.raw.ts = ts(l, start=c(2005,1), end=c(2010,23), frequency=23)*

* plot(MODIS.raw.ts)*

* l=cellStats(MODIS.filtered, stat=mean)*

* MODIS.filt.ts = ts(l, start=c(2005,1), end=c(2010,23), frequency=23)*

* plot(MODIS.filt.ts)*

One may find out the perfect fitting parameters by looking at the whole area, playing with the parameters:

*l=cellStats(MODIS, stat=’mean’)*

* MODIS.ts = ts(l, start=2005, end=c(2010,23), frequency=23)*

* sg=sgolayfilt(MODIS.ts, p=3, n=9, ts=20)*

* sg.ts = ts(sg, start=2005, end=c(2010,23), frequency=23)*

*plot(sg.ts)*

**20**.

**11**