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Fill data gaps and smooth outliers in a time series of satellite images

Source: R/rsat_smoothing_images.R
rsat_smoothing_images.Rd

apply_ima is the implementation of a spatio-temporal method called Interpolation of Mean Anomalies(IMA) for gap filling and smoothing satellite data (Militino et al. 2019) . smoothing_images is the implementation of a spatio temporal method called image mean anomaly (IMA) for gap filling and smoothing satellite data (Militino et al. 2019) .

Usage

rsat_smoothing_images(x, method, ...)

# S4 method for class 'rtoi,character'
rsat_smoothing_images(
  x,
  method,
  product = "ALL",
  satellite = "ALL",
  stage = "ALL",
  variable = "ALL",
  test.mode = FALSE,
  ...
)

# S4 method for class 'SpatRaster,character'
rsat_smoothing_images(x, method, ...)

Arguments

x

rtoi or RastespatRaster containing a time series of satellite images.

method

character argument. Defines the method used for processing the images, e.a. "IMA".

...

arguments for nested functions:

  • Img2Fill a vector defining the images to be filled/smoothed.

  • r.dates a vector of dates for the layers in x. Mandatory when layer names of x do not contain their capturing dates "YYYYJJJ" format.

  • nDays a numeric argument with the number of previous and subsequent days of the temporal neighborhood.

  • nYears a numeric argument with the number of previous and subsequent years of the temporal neighborhood.

  • aFilter a vector of lower and upper quantiles defining the outliers in the anomalies. Ex. c(0.05,0.95).

  • fact a numeric argument specifying the aggregation factor of the anomalies.

  • fun a function used to aggregate the image of anomalies. Both mean (default) or median are accepted.

  • snow.mode logical argument. If TRUE, the process is parallelized using the functionalities from the ` raster' package.

  • predictSE calculate the standard error instead the prediction.

  • factSE the fact used in the standard error prediction.

  • out.name the name of the folder containing the smoothed/filled images when saved in the Hard Disk Device (HDD).

  • only.na logical argument. If TRUE only fills the NA values. FALSE by default.

product

character argument. The name of the product to be processed. Check the name of the parameter with rsat_list_data function. Check the name of the parameter with rsat_list_data function. By default, "ALL".

satellite

character argument. The name of the satellite to be processed. Check the name of the parameter with rsat_list_data function. By default, "ALL".

stage

character argument. The name of the processed stage of the data. Check the name of the parameter with rsat_list_data function. By default, "ALL".

variable

character argument.The name of the variable to be processed. Check the name of the parameter with rsat_list_data function. By default, "ALL".

test.mode

logical argument. If TRUE, the function runs some lines to test rsat_smoothing_images with rtoi object.

Value

a RastespatRaster with the filled/smoothed images.

Details

This filling/smoothing method was developed by Militino et al. (2019) . IMA fills the gaps borrowing information from an adaptable temporal neighborhood. Two parameters determine the size of the neighborhood; the number of days before and after the target image (nDays) and the number of previous and subsequent years (nYears). Both parameters should be adjusted based on the temporal resolution of the of the time-series of images. We recommend that the neighborhood extends over days rather than years, when there is little resemblance between seasons. Also, cloudy series may require larger neighborhoods.

IMA gives the following steps; (1) creates a representative image from the temporal neighborhood of the target image (image to be filled/smoothed) e.g., doing the mean, median, etc. for each pixel's time-series (fun), (2) the target and representative images are subtracted giving an image of anomalies, (3) the anomalies falling outside the quantile limits (aFilter) are considered outliers and therefore removed, (4) it aggregates the anomaly image into a coarser resolution (fact) to reveal potential spatial dependencies, (5) the procedure fits a spatial model (thin plate splines or TPS) to the anomalies which is then used to interpolate the values at the original resolution, and (6) the output is the sum of the interpolated anomalies and the average image.

References

Militino AF, Ugarte MD, Perez-Goya U, Genton MG (2019). “Interpolation of the Mean Anomalies for Cloud-Filling in Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI).” IEEE Transactions on Geoscience and Remote Sensing. (Open-Access). http://dx.doi.org/10.1109/TGRS.2019.2904193.

Examples

if (FALSE) { # \dontrun{
## Smooth data in rtoi
library(rsat)
require(terra)

# create a copy of pamplona in temp file
file.copy(from=system.file("ex/PamplonaDerived",package="rsat"),
         to=tempdir(),
         recursive = TRUE)

# load example rtoi
pamplona <- read_rtoi(file.path(tempdir(),"PamplonaDerived"))
rsat_smoothing_images(pamplona,
                      method = "IMA",
                      variable="NDVI"
)
rsat_list_data(pamplona)
# get smoothed
smoothed <- rsat_get_SpatRaster(pamplona,p="mod09ga",v="NDVI",s="IMA")
plot(smoothed)

# get original
original <- rsat_get_SpatRaster(pamplona,p="mod09ga",v="NDVI",s="variables")
plot(original)
plot(smoothed[[1]]-original[[1]])

## smooth user defined SpatRaster dataset
require(terra)
data(ex.ndvi.navarre)

# load an example of NDVI time series in Navarre
ex.ndvi.navarre <- rast(ex.ndvi.navarre)
# the raster stack with the date in julian format as name
plot(ex.ndvi.navarre)

# smoothin and fill all the time series
tiles.mod.ndvi.filled <- rsat_smoothing_images(ex.ndvi.navarre,
  method = "IMA"
)
# show the filled images
plot(tiles.mod.ndvi.filled)

# plot comparison of the cloud and the filled images
tiles.mod.ndvi.comp <- c(
  ex.ndvi.navarre[[1]], tiles.mod.ndvi.filled[[1]],
  ex.ndvi.navarre[[2]], tiles.mod.ndvi.filled[[2]]
)
plot(tiles.mod.ndvi.comp)
} # }