genSmoothingIMA: Fill data gaps and smooth outliers in a time series of...
In RGISTools: Handling Multiplatform Satellite Images

Description Usage Arguments Details Value References Examples

genSmoothingIMA is the implementation of a spatio temporal method called image mean anomaly (IMA) for gap filling and smoothing satellite data \insertCitemilitino2019interpolationRGISTools.

genSmoothingIMA(
  rStack,
  Img2Fill = NULL,
  nDays = 3,
  nYears = 1,
  fact = 5,
  fun = mean,
  r.dates,
  aFilter = c(0.05, 0.95),
  only.na = FALSE,
  factSE = 8,
  predictSE = FALSE,
  snow.mode = FALSE,
  out.name = "outname",
  ...
)

`rStack`	a `RasterStack` class argument containing a time series of satellite images. Layer names should contain the date of the image in "`YYYYJJJ`" format.
`Img2Fill`	a `vector` argument defining the images to be filled/smoothed.
`nDays`	a `numeric` argument with the number of previous and subsequent days that define the temporal neighborhood.
`nYears`	a `numeric` argument with the number of previous and subsequent years that define the temporal neighborhood.
`fact`	a `numeric` argument with an aggregation factor of the anomalies before the interpolation.
`fun`	a `function` used to aggregate the image of anomalies. Both `mean` (default) or `median` are acceptted.
`r.dates`	a `vector` argument containing the dates of the layers in rstack
`aFilter`	a `vector` with the lower and upper quantiles that define the outliers of the anomalies. Ex. c(0.05,0.95).
`only.na`	logical argument. If `TRUE` only fills the `NA` values. `FALSE` by default.
`factSE`	the `fact` used in the standard error prediction.
`predictSE`	calculate the standard error instead the prediction.
`snow.mode`	logical argument. If `TRUE`, the filling process will be parallelized using the '`raster`' package.
`out.name`	the name of the folder containing the smoothed/filled images when saved in the Hard Disk Device (HDD).
`...`	arguments for nested functions: `AppRoot` the path where the filled/smoothed time series of images will be saved in GTiff format.

This filling/smoothing method was developed by \insertCitemilitino2019interpolation;textualRGISTools. This technique decomposes a time series of images into a new series of mean and anomaly images. The procedure applies the smoothing algorithm over the anomaly images. The procedure requires a proper definition of a temporal neighbourhood for the target image and aggregation factor.

a RasterStack with the filled/smoothed images.

\insertRef

militino2019interpolationRGISTools

## Not run: 
# load an example of NDVI time series in Navarre
data(ex.ndvi.navarre)
# the 2 images to be filled and the neighbourhood
genPlotGIS(ex.ndvi.navarre)

# filled images
tiles.mod.ndvi.filled  <- genSmoothingIMA(ex.ndvi.navarre,
                               Img2Fill = c(1),
                               only.na=TRUE)
# show the filled images
genPlotGIS(tiles.mod.ndvi.filled)
# plot comparison of the cloud and the filled images
tiles.mod.ndvi.comp <- stack(ex.ndvi.navarre[[1]], tiles.mod.ndvi.filled[[1]],
                             ex.ndvi.navarre[[2]], tiles.mod.ndvi.filled[[2]])
genPlotGIS(tiles.mod.ndvi.comp, layout=c(2, 2))

## End(Not run)